--------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/logs/3_analysi
> s.log
  log type:  text
 opened on:   3 Mar 2024, 12:18:49

.         
.         // list dofiles in alphabetical order (saved in local files) 
.         local files : dir "$do_path/3_analysis/" files "*.do"

.         local files: list sort files

.         macro list _files
_files:         "01a_descriptives_address_level_figures_4_5_d1_d2_d4.do"
                "01b_descriptives_precinct_level_figures_1_d3_d5_d6.do"
                "01c_descriptives_precinct_maps_figure_d7.do"
                "01d_special_voting_arrangements_idea_stats.do"
                "01e_descriptives_representativeness_munich_table_e1.do"
                "02_sumstats_and_balance_figure_6_tables_e2_e3_e4.do" "03a_main_figure_7.do"
                "03b_main_tables_1_c1_c2_c3_c4.do" "03c_party_outcomes_figures_11_d14.do"
                "03d_het_by_distance_figures_8_9.do"
                "03e_het_by_precinct_characs_figures_10_d13_tables_e6_e7.do"
                "04a_rob_het_by_distance_figures_d8_d10.do"
                "04b_rob_het_by_distance_no_ambiguity_figure_d9.do"
                "04c_rob_party_district_level_figure_d15.do" "04d_rob_het_by_reason_figure_c2.do"
                "04e_rob_noveldid_figures_c1_d11_d12_table_e5.do"
                "04f_rob_honestdid_figures_c6_c7.do" "04g_rob_matching_figures_c3_c4_c5.do"
                "04h_rob_covariates_tables_c5_c6.do"
                "04i_rob_alt_treatm_figures_c8_c9_c10_c11_table_c7.do"
                "04j_rob_balanced_smpl_tables_e8_e9.do" "05_school_construction_programm_stats.do"
                "06_conceptual_framework_figures_b2_b3.do"

.         
.         // run dofiles
.         foreach f of local files {
  2.                 di as text "Running: `f'"
  3.                 do "$do_path/3_analysis/`f'"
  4.         }
Running: 01a_descriptives_address_level_figures_4_5_d1_d2_d4.do

. /*
> Input:  newdata/estimation_prep_ltw18_voter [prepared address-level panel]
>                 newdata/wahllokal_change                   [prepared polling place level panel]
> 
> Output:
>         - Figures 4, 5, D.1, D.2, D.4
>         
> Tasks: Descriptives at the VOTER-ADDRESS level and POLLING PLACE level
>         
> */      
. 
. 
.                 
. ********************************************************************************
.                                                 // Voter-address level stats //
. ********************************************************************************
. 
. * PULL: Address-level data
.         use "$newdata/estimation_prep_ltw18_voter.dta", clear

.         
.         * PLOT: FIGURE D4. Frequency of Polling Place Reassignments per Residential Address
. frame copy default tmp, replace

. frame tmp {
.         cap drop tmp*
.         
.         bys voter_id: egen tmp_nbr_reassigned = total(treat_simple) // tot nbr of reassignments
.         
.         tab tmp_nbr_reassigned   // tab #reassignments per address

tmp_nbr_rea |
    ssigned |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    513,560       42.58       42.58
          1 |    314,440       26.07       68.64
          2 |    293,408       24.32       92.97
          3 |     64,456        5.34       98.31
          4 |     13,472        1.12       99.43
          5 |      6,896        0.57      100.00
------------+-----------------------------------
      Total |  1,206,232      100.00
.         
.         // Among those reassigned 1+, how often is this a return to an old PP?
.         bys voter_id: egen tmp_totnew= total(changed_wl) // tot nbr of change to NEW PP
.         tab tmp_totnew if tmp_nbr_reassigned==1                                 

 tmp_totnew |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    314,440      100.00      100.00
------------+-----------------------------------
      Total |    314,440      100.00
.         tab tmp_totnew if tmp_nbr_reassigned==2                                 

 tmp_totnew |      Freq.     Percent        Cum.
------------+-----------------------------------
          2 |    293,408      100.00      100.00
------------+-----------------------------------
      Total |    293,408      100.00
.         tab tmp_totnew if tmp_nbr_reassigned==3                                         

 tmp_totnew |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |     64,456      100.00      100.00
------------+-----------------------------------
      Total |     64,456      100.00
.         tab tmp_totnew if tmp_nbr_reassigned==4                                         

 tmp_totnew |      Freq.     Percent        Cum.
------------+-----------------------------------
          4 |     13,472      100.00      100.00
------------+-----------------------------------
      Total |     13,472      100.00
.         
.         // For those reassigned TWICE, how long is the period b/w first and second reassignment?
.         gen tmp_neg_wahl_id = -wahl_id
.         bys voter_id (treat_simple tmp_neg_wahl_id): gen tmp_first = wahl_id[_N] if tmp_nbr_reas
> signed>1
(828,000 missing values generated)
.         bys voter_id (treat_simple tmp_neg_wahl_id): gen tmp_second = wahl_id[_N-1] if tmp_nbr_r
> eassigned>1
(828,000 missing values generated)
.         gen tmp_period = tmp_second-tmp_first
(828,000 missing values generated)
.         
.         tab tmp_period if tmp_nbr_reassigned==2 

 tmp_period |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |     74,240       25.30       25.30
          2 |     36,808       12.54       37.85
          3 |    159,800       54.46       92.31
          4 |     11,048        3.77       96.08
          5 |     10,168        3.47       99.54
          6 |          8        0.00       99.54
          7 |      1,336        0.46      100.00
------------+-----------------------------------
      Total |    293,408      100.00
.         tab tmp_period if tmp_nbr_reassigned>1

 tmp_period |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    129,224       34.17       34.17
          2 |     54,536       14.42       48.58
          3 |    159,800       42.25       90.83
          4 |     20,136        5.32       96.16
          5 |     11,288        2.98       99.14
          6 |      1,912        0.51       99.65
          7 |      1,336        0.35      100.00
------------+-----------------------------------
      Total |    378,232      100.00
.         su  tmp_period if tmp_nbr_reassigned==2, det 

                         tmp_period
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs             293,408
25%            1              1       Sum of wgt.     293,408

50%            3                      Mean           2.493756
                        Largest       Std. dev.       1.06332
75%            3              7
90%            3              7       Variance        1.13065
95%            4              7       Skewness        .253089
99%            5              7       Kurtosis       3.712376
.         su  tmp_period if tmp_nbr_reassigned>1, det 

                         tmp_period
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs             378,232
25%            1              1       Sum of wgt.     378,232

50%            3                      Mean           2.314727
                        Largest       Std. dev.      1.147095
75%            3              7
90%            3              7       Variance       1.315828
95%            4              7       Skewness       .5326905
99%            5              7       Kurtosis       3.310065
.         
.         // keep one obs per voter
.         bys voter_id: keep if _n ==1
(1,055,453 observations deleted)
.         su tmp_nbr_reassigned, det

                     tmp_nbr_reassigned
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs             150,779
25%            0              0       Sum of wgt.     150,779

50%            1                      Mean           .9807334
                        Largest       Std. dev.      1.036517
75%            2              5
90%            2              5       Variance       1.074368
95%            3              5       Skewness       .8751158
99%            4              5       Kurtosis       3.435033
.         local p50 = r(p50)
.         local avg = r(mean)
.         
.         * PLOT  
.         twoway  (hist tmp_nbr_reassigned ,      percent fcol(%0) discrete gap(2) ) ///
>                         , scheme (plotplain) xline(`avg', lcol(maroon) lpat(solid)) ///
>                         xtitle("Number of reassignments per address between 2013 and 2020") aspe
> ct(.5) name(freq, replace)
. 
.         graph export "$figures/Figure_D4_voter_reassignment_freq.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D4_vot
    > er_reassignment_freq.pdf saved as PDF format
. }       

. 
. 
. * Treatment share over time: Stacked bar plot by reason of reassignment
. frame copy default tmp, replace

. frame tmp {
.         collapse (mean) treat_*, by(wahl_id)
. 
.         label define wahl_id 1 "State 2013", modify
.         label define wahl_id 2 "Federal 2013", modify
.         label define wahl_id 3 "Municipal 2014", modify
.         label define wahl_id 4 "European 2014", modify
.         label define wahl_id 5 "Federal 2017", modify
.         label define wahl_id 6 "State 2018", modify
.         label define wahl_id 7 "European 2019", modify
.         label define wahl_id 8 "Municipal 2020", modify
.         
.         replace treat_no_consol = treat_no_consol*100
(6 real changes made)
.         replace treat_consol = treat_consol*100
(6 real changes made)
.         
.         * PLOT: Figure 4. Share of reassigned addresses by reassigment reason
.         graph bar treat_no_consol treat_consol, over(wahl_id, relabel(1 `" "State" "2013" "' 2 `
> " "Federal" "2013" "' ///
>                 3 `" "Municipal" "2014" "' 4 `" "European" "2014" "' 5 `" "Federal" "2017" "' //
> /
>                 6 `" "State" "2018" "' 7 `" "European" "2019" "' 8 `" "Municipal" "2020" "' ) ga
> p(10))  ///
>                 stack title("") ytitle("% Reassigned home addresses", size(medsmall)) b1title("E
> lection") ///
>                 bargap(5) bar(2, fcolor(white) lcol(black)) bar(1, fcolor(black%50) lcol(black))
>  aspect(.5)  ///
>                 legend(label(1 "due polling location recruitment") label(2 "due to precinct reco
> nfiguration") ///
>                 col(1) pos(9) ring(0) title("Polling place reassignments", size(small) justifica
> tion(left) bexpand))
. 
.         graph export "$figures/Figure_4_bar_share_treated2_HNR_over_time.pdf", replace  
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_4_bar_
    > share_treated2_HNR_over_time.pdf saved as PDF format
. }

. 
.         
.  * Density: Walking Distance
.         // walking distance, overall
.         su street_dist,det 

                   walking distance in km
-------------------------------------------------------------
      Percentiles      Smallest
 1%        .1089              0
 5%         .231              0
10%        .3107              0       Obs           1,206,232
25%        .4816              0       Sum of wgt.   1,206,232

50%        .7352                      Mean           .8161009
                        Largest       Std. dev.      .4580448
75%       1.0693         5.0616
90%       1.4249         5.0616       Variance        .209805
95%       1.6629         5.1641       Skewness       1.192675
99%       2.1786         5.2779       Kurtosis       6.005761

.         local p50=r(mean)

.         tw (hist street_dist, fcol(%0) xaxis(1 2) xla(`p50' `p50', axis(2) notick format(%3.2f))
>  ) ///
>                 , xtitle(Walking distance to polling place (km)) xtitle("", axis(2))  xline(`p50
> ' , lcol(maroon) lwidth(.3) lpat(solid)) ///
>                  legend(off) name(dist, replace)  aspect(.7) xlab(#8)

.         
.         // CHANGE in walking distance conditional on reassignment
.         su del_street_dist if treat_no_consol == 1,det

          Change in street_dist (due to PP change)
-------------------------------------------------------------
      Percentiles      Smallest
 1%      -1.2241        -3.3313
 5%       -.7874        -3.3313
10%       -.5405        -3.3313       Obs              87,704
25%      -.21105        -3.3313       Sum of wgt.      87,704

50%        .0404                      Mean           .0598241
                        Largest       Std. dev.       .509764
75%        .3527         2.4266
90%         .698         2.4428       Variance       .2598594
95%        .8864          2.453       Skewness      -.0379072
99%       1.4006         2.7076       Kurtosis       4.664136

.         su del_street_dist if treat_consol == 1,det

          Change in street_dist (due to PP change)
-------------------------------------------------------------
      Percentiles      Smallest
 1%      -1.3312        -2.4332
 5%       -.8735          -2.36
10%      -.64105          -2.36       Obs              60,170
25%        -.277          -2.36       Sum of wgt.      60,170

50%        .0327                      Mean           .0470642
                        Largest       Std. dev.      .5606112
75%        .3666         3.7206
90%        .7465         3.7206       Variance       .3142849
95%        .9576         3.7206       Skewness       .2123816
99%       1.3678         3.8442       Kurtosis       5.415368

.         su del_street_dist if treat_simple!=0,det

          Change in street_dist (due to PP change)
-------------------------------------------------------------
      Percentiles      Smallest
 1%      -1.2723        -3.3313
 5%       -.8151        -3.3313
10%       -.5796        -3.3313       Obs             147,874
25%       -.2423        -3.3313       Sum of wgt.     147,874

50%       .03675                      Mean           .0546321
                        Largest       Std. dev.      .5310767
75%        .3591         3.7206
90%        .7139         3.7206       Variance       .2820424
95%        .9198         3.7206       Skewness       .0784108
99%       1.3876         3.8442       Kurtosis       5.078594

.         local p50=r(mean)

.         
.         * PLOT: FIGURE 5. Density plots of walking distance and change in distance
.         tw              (hist del_street_dist if treat_no_consol == 1 , fcol(black%40) lcol(%0) 
> xaxis(1 2) xla(`p50' `p50', axis(2) notick format(%3.2f)) ) ///
>                         (hist del_street_dist if treat_consol ==1 , fcol(%0))  ///
>                 , xtitle("Change in walking distance after reassignment (km)") ///
>                 xtitle("", axis(2))  ytitle("  ") xline(`p50' , lcol(maroon) lwidth(.3) lpat(sol
> id) ) ///
>                 legend(order(1 "due to polling location" "recruitment" 2 "due to precinct" "reco
> nfiguration") ring(0) pos(1) title("Polling place reassignment", size(small))) ///
>                 xlab(#8) name(chdist, replace)   aspect(.7)     

.         
.         graph combine dist chdist, ycommon imargin(zero)

.         graph export "$figures/Figure_5_Density_distances_street.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_5_Dens
    > ity_distances_street.pdf saved as PDF format

.         
.         // Change in distance in absolute terms
.         gen del_street_dist_abs = abs(del_street_dist)

.         su del_street_dist_abs if treat_simple!=0,det

                     del_street_dist_abs
-------------------------------------------------------------
      Percentiles      Smallest
 1%        .0048              0
 5%        .0277              0
10%         .049              0       Obs             147,874
25%        .1366              0       Sum of wgt.     147,874

50%        .3047                      Mean           .3987601
                        Largest       Std. dev.       .354988
75%         .568         3.7206
90%        .8638         3.7206       Variance       .1260165
95%       1.0794         3.7206       Skewness       1.746279
99%        1.535         3.8442       Kurtosis       8.910692

.         
.                 
. ********************************************************************************
.                                                 // POLLING PLACE level stats //
. ********************************************************************************
. 
.         gdistinct wl_id                         // 293 distinct WL venues

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |    1206232        293

.         bys wahl_id: distinct wl_id

--------------------------------------------------------------------------------------------------
-> wahl_id = LTW13

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |     150779        197

--------------------------------------------------------------------------------------------------
-> wahl_id = BTW13

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |     150779        197

--------------------------------------------------------------------------------------------------
-> wahl_id = KOW14

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |     150779        196

--------------------------------------------------------------------------------------------------
-> wahl_id = EUW14

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |     150779        196

--------------------------------------------------------------------------------------------------
-> wahl_id = BTW17

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |     150779        220

--------------------------------------------------------------------------------------------------
-> wahl_id = LTW18

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |     150779        211

--------------------------------------------------------------------------------------------------
-> wahl_id = EUW19

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |     150779        204

--------------------------------------------------------------------------------------------------
-> wahl_id = KOW20

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |     150779        221

.         by wahl_id: distinct sb_old     // #unharmonized precincts

--------------------------------------------------------------------------------------------------
-> wahl_id = LTW13

        |        Observations
        |      total   distinct
--------+----------------------
 sb_old |     150779        702

--------------------------------------------------------------------------------------------------
-> wahl_id = BTW13

        |        Observations
        |      total   distinct
--------+----------------------
 sb_old |     150779        702

--------------------------------------------------------------------------------------------------
-> wahl_id = KOW14

        |        Observations
        |      total   distinct
--------+----------------------
 sb_old |     150779        702

--------------------------------------------------------------------------------------------------
-> wahl_id = EUW14

        |        Observations
        |      total   distinct
--------+----------------------
 sb_old |     150779        702

--------------------------------------------------------------------------------------------------
-> wahl_id = BTW17

        |        Observations
        |      total   distinct
--------+----------------------
 sb_old |     150779        617

--------------------------------------------------------------------------------------------------
-> wahl_id = LTW18

        |        Observations
        |      total   distinct
--------+----------------------
 sb_old |     150779        618

--------------------------------------------------------------------------------------------------
-> wahl_id = EUW19

        |        Observations
        |      total   distinct
--------+----------------------
 sb_old |     150779        618

--------------------------------------------------------------------------------------------------
-> wahl_id = KOW20

        |        Observations
        |      total   distinct
--------+----------------------
 sb_old |     150779        755

.         
.         
.         // precincts per WL                                                                     
>                                                 
.         di 618/211 //  LTW2018: 2.92891
2.92891

.         di 702/197 //  LTW2013: 3.56345
3.5634518

.         di 755/221 //  KOW2020: 3.4162896
3.4162896

.         di 702/196 //  KOW2014: 3.5816327
3.5816327

.         di 618/204 //  EUW2019: 3.02941
3.0294118

.         di 702/196 //  EUW2014: 3.58163
3.5816327

.         di 617/220 //  BTW2017: 2.8045
2.8045455

.         di 702/197 //  BTW2013: 3.563
3.5634518

.         
.         * PLOT: FIGURE D1. Types of Polling Venues
. frame copy default tmp, replace

. frame tmp {
.         gduplicates drop wl_id, force

Duplicates in terms of wl_id

(1,205,939 observations deleted)
.         gen obs = 1
.         collapse (count) obs, by(wl_kat)
.         gsort -obs
.         gen order = _n
.         replace order = 10 if wl_kat == "Other"
(1 real change made)
.         
.         su obs

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
         obs |          7    41.85714    74.30439          2        209
.         gen pct = 100*(obs/r(sum) )
.         
.         encode wl_kat, gen(tmp)
.         replace tmp = tmp + 99 if wl_kat == "Other"
(1 real change made)
.         labmask tmp, values(wl_kat)
.         
.         graph bar  pct, over(wl_kat, sort(order) ) intensity(0) horizontal scheme(plotplain) ///
>         ytitle("Percent") aspect(.4) ytick(0(10)80, grid) 
.         graph export "$figures/Figure_D1_WL_categories.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D1_WL_
    > categories.pdf saved as PDF format
. 
. }

. 
. 
. * PLOT: FIGURE D2. Activity Status of Polling Venues between 2009 and 2020
. frame copy default tmp, replace

. frame tmp {
.         
. * PULL: Polling place panel     
. use "$newdata/wahllokal_change",clear
.         
.         distinct wl_id if wahl_id>0 & wl_active==1 // 293

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |       1642        293
.         
.         keep wl_id wl_typ wahl_id wl_active
.         
.         expand 2 if wahl_id == 0, gen(new) // gen EuE-09 (assume same WL)
(299 observations created)
.         replace wahl_id = -1 if new==1
(299 real changes made)
.         drop new
.                 
.         cap drop tmp*
.         bys wl_id: egen tmp_cnt = total(wl_active) // total periods active
.         
.         gen tmp_wahl_id = (-1)*wahl_id
.         bys wl_id (wl_active tmp_wahl_id): gen tmp_first = wahl_id[_N] // wahl_id where first ac
> tive
.         replace tmp_first = (-1)*tmp_first // reverse for sorting
(2,990 real changes made)
.         
.         // sort precincts by never-treated > most frequently treated
.         egen tmp_id = group(tmp_first tmp_cnt wl_id)
.         
.         
.         // Plot 
.         heatplot wl_active i.tmp_id i.wahl_id, level(2) colors(gs15%30 black) ///
>         ylab(0(10)293, labsize(vsmall) nogrid) ysc(r(1,293)) xlab(1 `" "European" "2009" "' 2 `"
>  "Federal" "2009" "' 3 `" "State" "2013" "' 4 `" "Federal" "2013" "' ///
>                 5 `" "Municipal" "2014" "' 6 `" "European" "2014" "' 7 `" "Federal" "2017" "' //
> /
>                 8 `" "State" "2018" "' 9 `" "European" "2019" "' 10 `" "Municipal" "2020" "',lab
> size(2.7pt)  nogrid) ///
>         ytitle("Polling venue ID") xtitle("Election") scheme(plotplain) discrete  statistic(asis
> )  ///
>         ramp(lab(0 1) length(50) space(5))  aspect(0.55) xsc(titlegap(large)) 
.         
.         gr_edit .plotregion1.graph1.xaxis1.edit_tick 1 1 `" "European" "2009" "', tickset(major)
>  editstyle(tickstyle(textstyle(color(maroon))) )
.         gr_edit .plotregion1.graph1.xaxis1.edit_tick 2 2 `" "Federal" "2009" "', tickset(major) 
> editstyle(tickstyle(textstyle(color(maroon))) )
.         gr_edit .plotregion1.graph2.subtitle.text = {}
.         gr_edit .plotregion1.graph2.subtitle.text.Arrpush Polling venue active
.         gr_edit .plotregion1.graph2.subtitle.style.editstyle size(small) editcopy
.         
.         graph export "$figures/Figure_D2_heatmap_wl_active_2009_20.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D2_hea
    > tmap_wl_active_2009_20.pdf saved as PDF format
.         
. }       

. 
.         
. 
end of do-file
Running: 01b_descriptives_precinct_level_figures_1_d3_d5_d6.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output:
>         - Figures 1, D.3, D.5, D.6
>         
> Tasks: Descriptives at the precinct level
>         
>         
>         
> */      
. 
. * PULL: precinct-level panel
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
. ********************************************************************************
. * Prep
.                 
.         xtset sb_new wahl_id    

Panel variable: sb_new (strongly balanced)
 Time variable: wahl_id, 1 to 8
         Delta: 1 unit

. 
.         // english labels
.         label define wahl_id 1 "State (2013)", modify

.         label define wahl_id 2 "Federal (2013)", modify

.         label define wahl_id 3 "Municipal (2014)", modify

.         label define wahl_id 4 "European (2014)", modify

.         label define wahl_id 5 "Federal (2017)", modify

.         label define wahl_id 6 "State (2018)", modify

.         label define wahl_id 7 "European (2019)", modify

.         label define wahl_id 8 "Municipal (2020)", modify

.                 
. 
.         * gen wahl_date := Election date
.         gen     wahl_date = mdy(9,27,2009) if wahl_id == 0
(4,944 missing values generated)

.         replace wahl_date = mdy(9,15,2013) if wahl_id == 1
(618 real changes made)

.         replace wahl_date = mdy(9,22,2013) if wahl_id == 2
(618 real changes made)

.         replace wahl_date = mdy(3,16,2014) if wahl_id == 3
(618 real changes made)

.         replace wahl_date = mdy(5,25,2014) if wahl_id == 4
(618 real changes made)

.         replace wahl_date = mdy(9,24,2017) if wahl_id == 5
(618 real changes made)

.         replace wahl_date = mdy(10,14,2018) if wahl_id == 6     
(618 real changes made)

.         replace wahl_date = mdy(5,26,2019) if wahl_id == 7
(618 real changes made)

.         replace wahl_date = mdy(3,15,2020) if wahl_id == 8
(618 real changes made)

.         
.         format wahl_date %d 

. 
. 
. * mean of outcomes, weighted by #eligible voters
.         su turnout_tot_req turnout_urne turnout_pos_req [aw=wahlber_gesamt]

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
turnout_to~q |   4,944     7710778    62.39885   14.80721   15.10321   91.71677
turnout_urne |   4,944     7710778    33.69375   9.199333   9.938712   55.85818
turnout_po~q |   4,944     7710778    28.70511   7.720665   4.014273   51.98697

. 
.         
.         * PLOT: FIGURE D3. Distribution of Precinct Size
. * FIG: Distribution of UNHARMONIZED precinct sizes
. frame copy default tmp, replace

. frame tmp {
.         // pull UNCONVERTED, NON-HARMONIZED data
.         use "$newdata/raw_stimmbez_full.dta", clear
.         
.         su wahlber_gesamt, det  

                   Nbr of eligible voters
-------------------------------------------------------------
      Percentiles      Smallest
 1%          620             95
 5%         1050            115
10%         1230            295       Obs               5,416
25%         1361            313       Sum of wgt.       5,416

50%         1456                      Mean           1423.703
                        Largest       Std. dev.      200.5541
75%         1526           2212
90%         1603           2225       Variance       40221.95
95%         1663           2278       Skewness      -1.658986
99%         1838           2378       Kurtosis       9.449044
.         sum wahlber_gesamt if  inlist(wahl,"BTW17", "LTW18", "EUW19", "KOW20"), d

                   Nbr of eligible voters
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1267           1065
 5%         1349           1134
10%         1387           1170       Obs               2,608
25%         1438           1186       Sum of wgt.       2,608

50%         1486                      Mean           1484.943
                        Largest       Std. dev.      85.74972
75%         1530           1976
90%         1577           2027       Variance       7353.014
95%         1613           2060       Skewness       .6680166
99%         1722           2212       Kurtosis       8.726932
.         sum wahlber_gesamt if  !inlist(wahl,"BTW17", "LTW18", "EUW19", "KOW20"), d

                   Nbr of eligible voters
-------------------------------------------------------------
      Percentiles      Smallest
 1%          512             95
 5%          847            115
10%         1055            295       Obs               2,808
25%       1276.5            313       Sum of wgt.       2,808

50%         1391                      Mean           1366.825
                        Largest       Std. dev.      253.0664
75%       1511.5           2155
90%         1644           2225       Variance        64042.6
95%         1702           2278       Skewness       -1.02203
99%         1891           2378       Kurtosis       5.795673
.         bys wahl: su wahlber_gesamt ew_ges

--------------------------------------------------------------------------------------------------
-> wahl = BTW13

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wahlber_ge~t |        702    1306.358    220.7268        115       1698
      ew_ges |        702    2061.587    407.0117        189       4588

--------------------------------------------------------------------------------------------------
-> wahl = BTW17

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wahlber_ge~t |        617     1496.94    59.07997       1242       2027
      ew_ges |        617    2502.728    434.2068       1901       6272

--------------------------------------------------------------------------------------------------
-> wahl = EUW14

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wahlber_ge~t |        702    1316.246     218.289        336       1742
      ew_ges |        702    2090.241    415.1879        486       4598

--------------------------------------------------------------------------------------------------
-> wahl = EUW19

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wahlber_ge~t |        618    1501.764     70.7431       1281       1752
      ew_ges |        618    2496.282    419.3903       1876       4862

--------------------------------------------------------------------------------------------------
-> wahl = KOW14

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wahlber_ge~t |        702    1548.986    259.8957        361       2378
      ew_ges |        702     2086.08    411.1093        419       4628

--------------------------------------------------------------------------------------------------
-> wahl = KOW20

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wahlber_ge~t |        755    1470.955    120.4799       1065       2212
      ew_ges |        755    2066.281    337.0992       1454       3999

--------------------------------------------------------------------------------------------------
-> wahl = LTW13

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wahlber_ge~t |        702    1295.712    219.1138         95       1680
      ew_ges |        702    2061.541    407.2235        157       4588

--------------------------------------------------------------------------------------------------
-> wahl = LTW18

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wahlber_ge~t |        618    1473.235    63.53502       1232       1757
      ew_ges |        618    2477.519    418.4133       1875       5230

. 
.         // Density Plot: Precinct sizes over all elections
.         su wahlber_gesamt, det

                   Nbr of eligible voters
-------------------------------------------------------------
      Percentiles      Smallest
 1%          620             95
 5%         1050            115
10%         1230            295       Obs               5,416
25%         1361            313       Sum of wgt.       5,416

50%         1456                      Mean           1423.703
                        Largest       Std. dev.      200.5541
75%         1526           2212
90%         1603           2225       Variance       40221.95
95%         1663           2278       Skewness      -1.658986
99%         1838           2378       Kurtosis       9.449044
.         local p50 = r(p50)
.         tw (hist wahlber_gesamt, fcol(%0)),  ytitle("Density") xtitle("Precinct size (# eligible
>  voters)") ///
>                 legend(off) aspect(.7) xline(`p50', lcol(maroon) lpat(solid)) name(overall, repl
> ace)
. 
.         // Density Plot: Precinct sizes over all elections
.         tw (hist wahlber_gesamt if inlist(wahl,"BTW17", "LTW18", "EUW19", "KOW20"), fcol(%0)) //
> /
>                 (hist wahlber_gesamt if  !inlist(wahl,"BTW17", "LTW18", "EUW19", "KOW20"), fcol(
> %50)) ///
>                 , ytitle("Density")  xtitle("Precinct size (# eligible voters)") ///
>                 legend(lab(1 "After 2017") lab(2 "Before 2017") ring(0) pos(10)) aspect(.7) name
> (split, replace)
.                 
.                 
.         graph combine overall split,    xcommon ycommon imargin(0 1 0 0)
.         graph export "$figures/Figure_D3_nbr_eligible_voters_stimmbez.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D3_nbr
    > _eligible_voters_stimmbez.pdf saved as PDF format
. }       

.         
.         
. * Figure: Turnout and #EV over time
. frame copy default tmp, replace

. frame tmp {
.         
.         * AGGREGATE to city level (checked: aggregation matches city-level official stats)      
.         bys wahl_id stadtbez: egen sbez_wahlber_gesamt = total(wahlber_gesamt)
.         duplicates drop stadtbez wahl_id, force

Duplicates in terms of stadtbez wahl_id

(4,744 observations deleted)
.         collapse (rawsum)  wahlber_gesamt=sbez_wahlber_gesamt (mean) sbez_turnout* [pw=sbez_wahl
> ber_gesamt], by(wahl_date wahl wahl_type)
. 
.         // rescale #eligble voters in THSD
.         replace wahlber_gesamt = wahlber_gesamt/1000 
(8 real changes made)
.         // rescale turnout [0-100]
.         foreach v of varlist sbez_turnout* {
  2.                 replace `v' = `v' * 100
  3.         }       
(8 real changes made)
(8 real changes made)
(8 real changes made)
(8 real changes made)
(8 real changes made)
(8 real changes made)
.         // gen: Share of in-person votes OHNE Wahlschein in total votes (not requested!)
.         gen shr_urne = 100*(sbez_turnout_urne_ohne / sbez_turnout_tot)
.         
.         //  variables for stats
.         gen shr_urne_mit_all =  100*(sbez_turnout_urne_mit / sbez_turnout_tot) // shr of poll vo
> ters w polling card in all votes
.         gen shr_compl_urne = 100*(sbez_turnout_urne_mit / sbez_turnout_pos_req) // shr of poll v
> oters w polling card in all EV w polling card
.         gen shr_compl_pos =     100*(sbez_turnout_pos / sbez_turnout_pos_req) // shr of mail vot
> ers in all EV w polling card
.         gen shr_compl_tot = 100*((sbez_turnout_pos + sbez_turnout_urne_mit) / sbez_turnout_pos_r
> eq) // shr of mail and poll voters w polling card in all voters w PC
.         gen shr_urne_mit =  100*(sbez_turnout_urne_mit / (sbez_turnout_pos + sbez_turnout_urne_m
> it)) // shr of poll voters w PC in all votes w PC
. 
.         list wahl shr_* sbez_turnout_tot, abb(20)

     +--------------------------------------------------------------------------------------+
  1. |  wahl | shr_urne | shr_urne_mit_all | shr_compl_urne | shr_compl_pos | shr_compl_tot |
     | LTW13 | 59.61264 |         .8153961 |       1.798993 |      87.30685 |      89.10585 |
     |--------------------------------------------------------------------------------------|
     |              shr_urne_mit              |              sbez_turnout_tot               |
     |                  2.018939              |                      62.69583               |
     +--------------------------------------------------------------------------------------+

     +--------------------------------------------------------------------------------------+
  2. |  wahl | shr_urne | shr_urne_mit_all | shr_compl_urne | shr_compl_pos | shr_compl_tot |
     | BTW13 | 59.24969 |         .6262965 |        1.45018 |      92.90649 |      94.35667 |
     |--------------------------------------------------------------------------------------|
     |              shr_urne_mit              |              sbez_turnout_tot               |
     |                  1.536913              |                      71.22794               |
     +--------------------------------------------------------------------------------------+

     +--------------------------------------------------------------------------------------+
  3. |  wahl | shr_urne | shr_urne_mit_all | shr_compl_urne | shr_compl_pos | shr_compl_tot |
     | KOW14 | 58.61246 |         .6460988 |       1.377206 |       86.8433 |       88.2205 |
     |--------------------------------------------------------------------------------------|
     |              shr_urne_mit              |              sbez_turnout_tot               |
     |                  1.561095              |                      42.01775               |
     +--------------------------------------------------------------------------------------+

     +--------------------------------------------------------------------------------------+
  4. |  wahl | shr_urne | shr_urne_mit_all | shr_compl_urne | shr_compl_pos | shr_compl_tot |
     | EUW14 | 55.86988 |         .4941536 |       1.039479 |      91.79066 |      92.83014 |
     |--------------------------------------------------------------------------------------|
     |              shr_urne_mit              |              sbez_turnout_tot               |
     |                  1.119765              |                      45.75116               |
     +--------------------------------------------------------------------------------------+

     +--------------------------------------------------------------------------------------+
  5. |  wahl | shr_urne | shr_urne_mit_all | shr_compl_urne | shr_compl_pos | shr_compl_tot |
     | BTW17 | 56.63698 |         .7724233 |       1.702992 |      93.90117 |      95.60416 |
     |--------------------------------------------------------------------------------------|
     |              shr_urne_mit              |              sbez_turnout_tot               |
     |                  1.781295              |                      78.49519               |
     +--------------------------------------------------------------------------------------+

     +--------------------------------------------------------------------------------------+
  6. |  wahl | shr_urne | shr_urne_mit_all | shr_compl_urne | shr_compl_pos | shr_compl_tot |
     | LTW18 | 56.90201 |         .6730829 |       1.477114 |      93.10359 |      94.58071 |
     |--------------------------------------------------------------------------------------|
     |              shr_urne_mit              |              sbez_turnout_tot               |
     |                   1.56175              |                      72.73002               |
     +--------------------------------------------------------------------------------------+

     +--------------------------------------------------------------------------------------+
  7. |  wahl | shr_urne | shr_urne_mit_all | shr_compl_urne | shr_compl_pos | shr_compl_tot |
     | EUW19 | 53.92589 |         .7846664 |        1.60464 |      92.61677 |      94.22141 |
     |--------------------------------------------------------------------------------------|
     |              shr_urne_mit              |              sbez_turnout_tot               |
     |                  1.703053              |                       65.4179               |
     +--------------------------------------------------------------------------------------+

     +--------------------------------------------------------------------------------------+
  8. |  wahl | shr_urne | shr_urne_mit_all | shr_compl_urne | shr_compl_pos | shr_compl_tot |
     | KOW20 | 46.33529 |          1.27373 |       2.101299 |       86.4305 |      88.53179 |
     |--------------------------------------------------------------------------------------|
     |              shr_urne_mit              |              sbez_turnout_tot               |
     |                  2.373496              |                      49.02559               |
     +--------------------------------------------------------------------------------------+
.         
.         // ticks
.         forvalues y = 2013/2020 {
  2.                 local jan `jan' `=mdy(1,1,`y')'
  3.                 local jul `jul' `=mdy(7,1,`y')'
  4.         }
.         local jan `jan' `=mdy(1,1,2021)'
.         
.         // adjust location of bars
.         replace wahl_date = mdy(3,15,2013) if wahl == "LTW13"
(1 real change made)
.         replace wahl_date = mdy(9,15,2013) if wahl == "BTW13"
(1 real change made)
.         replace wahl_date = mdy(3,15,2014) if wahl == "KOW14"
(1 real change made)
.         replace wahl_date = mdy(9,15,2014) if wahl == "EUW14"
(1 real change made)
.         replace wahl_date = mdy(7,1,2017) if wahl == "BTW17"
(1 real change made)
.         replace wahl_date = mdy(7,1,2018) if wahl == "LTW18"
(1 real change made)
.         replace wahl_date = mdy(7,1,2019) if wahl == "EUW19"
(1 real change made)
.         replace wahl_date = mdy(7,1,2020) if wahl == "KOW20"
(1 real change made)
.         
.         // set barwidth
.         local barwidth 100
.         local tmp = `barwidth'/2
.         
.         // prep for fill pattern
.         expand 3 if wahl_type == 2, gen(exp)
(4 observations created)
.         replace wahl_type = . if exp == 1
(4 real changes made, 4 to missing)
.         bys wahl exp: replace wahl_date = wahl_date - `tmp' if exp ==1 & _n == 1
(2 real changes made)
.         bys wahl exp (wahl_date): replace wahl_date = wahl_date + `tmp' if exp ==1 & _n == _N
(2 real changes made)
.         
.         sort wahl_date
. 
.         * PLOT: FIGURE 1. Timeline and Turnout of Elections Held between 2013 and 2020
.         tw  (bar wahlber_gesamt wahl_date if wahl_type == 1, yaxis(1) yscale(r(0) axis(1)) ylabe
> l(0(200)1200) barwidth(`barwidth') fcolor(white) lcol(black)) ///
>                 (bar wahlber_gesamt wahl_date if wahl_type == 2, yaxis(1) yscale(r(0) axis(1)) y
> label(0(200)1200) barwidth(`barwidth') fcolor("scheme p6") lcol(black))  ///
>                 (bar wahlber_gesamt wahl_date if wahl_type == 3, yaxis(1) yscale(r(0) axis(1)) y
> label(0(200)1200) barwidth(`barwidth') fcolor("scheme p8") lcol(black)) ///
>                 (bar wahlber_gesamt wahl_date if wahl_type == 4, yaxis(1) yscale(r(0) axis(1)) y
> label(0(200)1200) barwidth(`barwidth') fcolor("scheme p1") lcol(black)) ///
>                 (scatter sbez_turnout_tot wahl_date if exp == 0, yaxis(2) ms(t) mcol(black) msiz
> e(large) yscale(r(0) axis(2)) ylabel(0(20)100, axis(2))) ///
>                 (line shr_urne wahl_date if exp == 0, yaxis(2) lpattern(solid) lcol(black)) ///
>                 (scatter shr_urne wahl_date if exp == 0, yaxis(2) ms(x) mcol(black)) ///
>         ,  ytitle("Eligible voters (thsd)", axis(1)) ytitle("Percent", axis(2)) xtitle("") ylab(
> , nogrid) xlab(,nogrid) ///
>          legend(lab(1 "Federal Election") lab(2 "European Election") lab(3 "Municipal Election")
>  ///
>                 lab(4 "State Election") lab(5 "Total turnout") lab(6 "Share of polling place vot
> es") pos(6) order(1 2 3 4 5 6) cols(2) rows(3)) ///
>                 xlabel(`jul',format(%tdCY) noticks) xtick(`jan') aspect(.6)
.         
.         graph export "$figures/Figure_1_wahlber_turnout_over_time.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_1_wahl
    > ber_turnout_over_time.pdf saved as PDF format
. }

. 
. 
. * PLOT: Treatment Timing at the Precinct level: TREATMENT = 100% reassigned
.         cap drop tmp*

.         cap drop _*

.         
.         bys sb_new: egen tmp_cnt = total(fulltreat_simple)

.         
.         // sort precincts by never-treated > most frequently treated
.         egen tmp_id = group(sb_new)

.         
.         gen     tmp_bi_treat = fulltreat_simple

.         
.         * PLOT: FIGURE D.6 Timing of Polling Place Reassignments
.         // heatplot
.         heatplot tmp_bi_treat i.tmp_id i.wahl_id,  colors(black%2 navy) ///
>         ylab(0(40)620, labsize(vsmall) nogrid) ysc(r(1,618)) xlab(1 `" "State" "2013" "' 2 `" "F
> ederal" "2013" "' ///
>                 3 `" "Municipal" "2014" "' 4 `" "European" "2014" "' 5 `" "Federal" "2017" "' //
> /
>                 6 `" "State" "2018" "' 7 `" "European" "2019" "' 8 `" "Municipal" "2020" "',labs
> ize(2.7pt)  nogrid) ///
>         ytitle("Precinct ID") xtitle("Election")  discrete  statistic(asis) ///
>         aspect(.6) xsc(titlegap(large)) levels(2) legend(pos(6))

.         
.         gr_edit .legend.subtitle.text = {}

.         gr_edit .legend.subtitle.text.Arrpush "Full precinct reassigned"

.         gr_edit .legend.plotregion1.label[1].text = {}

.         gr_edit .legend.plotregion1.label[1].text.Arrpush 1

.         gr_edit .legend.plotregion1.label[2].text = {}

.         gr_edit .legend.plotregion1.label[2].text.Arrpush 0

.         gr_edit .legend.plotregion1.DragBy 4 -21.5

.         gr_edit .legend.plotregion1.label[1].text = {}

.         gr_edit .legend.plotregion1.label[2].text = {}

.         gr_edit .legend.plotregion1.key[2].view.style.editstyle area(shadestyle(intensity(0))) e
> ditcopy

.         gr_edit .legend.plotregion1.key[2].view.style.editstyle area(linestyle(color("%0"))) edi
> tcopy

.         gr_edit .legend.plotregion1.key[2].view.style.editstyle area(shadestyle(color("%0"))) ed
> itcopy

.         gr_edit .legend.plotregion1.key[1].DragBy -0.15 0

.         gr_edit .legend.yoffset = 4

.         graph export "$figures/Figure_D6_heatmap_reassigned_bi.pdf", replace            
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D6_hea
    > tmap_reassigned_bi.pdf saved as PDF format

. 
. 
.                         
. * Distribution of # times precinct treated repeatedly over time?                                
.         cap drop tmp*

.         bys sb_new: egen tmp_tot_parttreat = total(parttreat_simple)

.         bys sb_new: egen tmp_tot_fulltreat = total(fulltreat_simple)

.         
.   // For: treatment=100% reassigned (FIGURE D.6)
.         tab tmp_tot_fulltreat if wahl_id==8 // 338 Never-treated units

tmp_tot_ful |
     ltreat |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        338       54.69       54.69
          1 |        150       24.27       78.96
          2 |        106       17.15       96.12
          3 |         22        3.56       99.68
          4 |          1        0.16       99.84
          5 |          1        0.16      100.00
------------+-----------------------------------
      Total |        618      100.00

.                                                                                 // 280 precincts
>  treated at least once                                                                          
.                                                                                 
. /* FIGURE D.6
> tmp_tot_ful |
>      ltreat |      Freq.     Percent        Cum.
> ------------+-----------------------------------
>           0 |        338       54.69       54.69
>           1 |        150       24.27       78.96
>           2 |        106       17.15       96.12
>           3 |         22        3.56       99.68
>           4 |          1        0.16       99.84
>           5 |          1        0.16      100.00
> ------------+-----------------------------------
>       Total |        618      100.00
> */
.                                                                                 
.         tab Ei if wahl_id==1            // 61.79 % first treated in BTW 2017

         date of |
treatment,treat= |
100% reassigned, |
            NT=. |      Freq.     Percent        Cum.
-----------------+-----------------------------------
    State (2013) |         10        3.57        3.57
Municipal (2014) |         10        3.57        7.14
  Federal (2017) |        173       61.79       68.93
    State (2018) |         35       12.50       81.43
 European (2019) |         12        4.29       85.71
Municipal (2020) |         40       14.29      100.00
-----------------+-----------------------------------
           Total |        280      100.00

.         /* FIGURE D.6
>                Ei    |      Freq.     Percent        Cum.
> -----------------+-----------------------------------
>     State (2013) |         10        3.57        3.57
> Municipal (2014) |         10        3.57        7.14
>   Federal (2017) |        173       61.79       68.93
>     State (2018) |         35       12.50       81.43
>  European (2019) |         12        4.29       85.71
> Municipal (2020) |         40       14.29      100.00
> -----------------+-----------------------------------
>            Total |        280      100.00
>         */
.         
.         // in 39.8% of all instances in which treat_simple>0, reassigned = 100% 
.                 su parttreat_simple

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
parttreat_~e |      4,944    .2168285    .4121265          0          1

.                 local pt = r(sum)

.                 su fulltreat_simple

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
fulltreat_~e |      4,944      .08839    .2838899          0          1

.                 local ft = r(sum)

.                 di `ft'/`pt'
.40764925

. 
. * Density plot of treatment intensity                                                           
>                                                                                                 
.         tabstat parttreat_simple fulltreat_simple, by(wahl_id)

Summary statistics: Mean
Group variable: wahl_id (Election ID (num, chronological))

         wahl_id |  parttr~e  fulltr~e
-----------------+--------------------
    State (2013) |   .118123  .0161812
  Federal (2013) |         0         0
Municipal (2014) |  .0355987  .0177994
 European (2014) |         0         0
  Federal (2017) |  .6634304  .2880259
    State (2018) |  .1731392  .1343042
 European (2019) |  .1165049  .0954693
Municipal (2020) |  .6278317  .1553398
-----------------+--------------------
           Total |  .2168285    .08839
--------------------------------------

.         
.         cap drop a50_simple

.         gen     a50_simple = (treat_simple >=0.5)

.         
.         su              parttreat_simple

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
parttreat_~e |      4,944    .2168285    .4121265          0          1

.         local   ps = r(sum)

.         su              fulltreat_simple

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
fulltreat_~e |      4,944      .08839    .2838899          0          1

.         local   shr_full = (r(sum)/`ps')*100

.         su              a50_simple

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  a50_simple |      4,944    .1312702    .3377298          0          1

.         local   shr_50= (r(sum)/`ps')*100

.         su              parttreat_consol

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
par~t_consol |      4,944    .1233819    .3289083          0          1

.         local   ps = r(sum)     

.         su              fulltreat_consol

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
ful~t_consol |      4,944    .0210356    .1435175          0          1

.         local   shr_full_cons = (r(sum)/`ps')*100

.         su              parttreat_no_consol

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
par~o_consol |      4,944    .1086165    .3111889          0          1

.         local   ps = r(sum)     

.         su              fulltreat_no_consol

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
ful~o_consol |      4,944    .0592638    .2361416          0          1

.         local   shr_full_no = (r(sum)/`ps')*100 

.         di "`:di %12.2f `shr_full'' % of instances where treat_simple>0 are 100% reassinged" // 
> 41% (K100 full)
       40.76 % of instances where treat_simple>0 are 100% reassinged

.         di "`:di %12.2f `shr_50'' % of instances where treat_simple>0 are 50% reassigned" // 61%
>  (K50)
       60.54 % of instances where treat_simple>0 are 50% reassigned

.         di "`:di %12.2f `shr_full_cons'' % of instances where treat_consol>0 are 100% reassinged
> " // 16% (K100 consol)
       17.05 % of instances where treat_consol>0 are 100% reassinged

.         di "`:di %12.2f `shr_full_no'' % of instances where treat_no_consol>0 are 100% reassigne
> d" // 54%        (K100 no_consol)
       54.56 % of instances where treat_no_consol>0 are 100% reassigned

.         
.         
.         * PLOT: FIGURE D.5 Reassignment Intensity at the Precinct Level
.         // Density Plot: treat_simple (all types of reassignments)
.         tw (hist treat_simple if treat_simple >0, percent fcol(%0)), ///
>                  xtitle("Share of reassigned addresses") ytitle("Percent")  ///
>                 title("Total")  legend(off) name(total, replace)  nodraw aspect(.7) 

.         
.         // Density: treat_consol (precinct reconfiguration)
.         tw (hist treat_consol if parttreat_consol==1, percent fcol(%0)),  ///
>                 xtitle("Share of reassigned addresses") ytitle("Percent") ///
>                 title("Precinct reconfiguration") legend(off) name(consol, replace) nodraw aspec
> t(.7)

.         
.         // Density: treat_no_consol (recruitment)
.         tw (hist treat_no_consol if parttreat_no_consol==1, percent fcol(%0)),  ///
>                 xtitle("Share of reassigned addresses") ytitle("Percent") legend(off) ///
>                 title("Recruitment of different venue") name(no_consol, replace) nodraw aspect(.
> 7)

. 
.         // combine and save
.         graph combine total no_consol consol,  ycommon xcommon row(1) imargin(zero)

.         graph export "$figures/Figure_D5_sb_treat_intensity.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D5_sb_
    > treat_intensity.pdf saved as PDF format

. 
end of do-file
Running: 01c_descriptives_precinct_maps_figure_d7.do

. /*
> 
> Input:  newdata/estimation_prep_ltw18_voter [prepared address-level panel]
>  
> Output: Figure D.7
> 
> Tasks: Produce maps
> 
> */
. 
. * PLOT: FIGURE D7. Spatial Distribution of Polling Place Reassignments (Event = 100% reassignmen
> t)
. forvalues j= 1/8 {      
  2.                 use "$newdata/estimation_prep_ltw18.dta", clear
  3.         
.                 keep if wahl_id==`j'
  4.                 
.                 replace fulltreat_simple= 0 if smpl_trim !=1
  5.                 
.                 merge 1:n sb_new using "$newdata/ltw18_sb_mapping.dta", assert(3) nogen
  6.         
.          spmap fulltreat_simple  using "$shp_path/landtagwahl2018_stimmbezirke_newprj_shp.dta", 
> ///
>         id(_ID) clmethod(unique) fcol(none cranberry%60 )  ///
>         osize(vthin ..) ocol(black%20) ndsize(vvthin) ndfcolor(black%3) name(w`j', replace) ///
>         polygon(data("$shp_path/stadtbezirke_shp.dta") ocol(black ..) osize(*1 ..)) ysize(3) ///
>         legend(order( 3 "= Polling place relocation (event)") size(medsmall))
  7.         
. }
(4,326 observations deleted)
(0 real changes made)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------
(4,326 observations deleted)
(0 real changes made)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------
(4,326 observations deleted)
(1 real change made)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------
(4,326 observations deleted)
(0 real changes made)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------
(4,326 observations deleted)
(5 real changes made)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------
(4,326 observations deleted)
(48 real changes made)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------
(4,326 observations deleted)
(47 real changes made)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------
(4,326 observations deleted)
(56 real changes made)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------

.         grc1leg w1 w3  w5 w6 w7 w8, col(2)

.         gr_edit .plotregion1.graph1.title.text.Arrpush "State Election 2013"

.         gr_edit .plotregion1.graph2.title.text.Arrpush "Municipal Election 2014"

.         gr_edit .plotregion1.graph3.title.text.Arrpush "Federal Election 2017"

.         gr_edit .plotregion1.graph4.title.text.Arrpush "State Election 2018"    

.         gr_edit .plotregion1.graph5.title.text.Arrpush "European Election 2019"

.         gr_edit .plotregion1.graph6.title.text.Arrpush "Municipal Election 2020"        

.         
.         graph export "$figures/Figure_D7_map_treatment_timing.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D7_map
    > _treatment_timing.pdf saved as PDF format

.         
.         
. 
.         
. 
.         
. 
end of do-file
Running: 01d_special_voting_arrangements_idea_stats.do

. /*
> 
> Input: Stats on special voting arrangements around the world, IDEA, November 2023
>         > cleaned_IDEA_postal_voting_data.xlsx [IDEA, postal voting arrangements]
>         > cleaned_IDEA_early_voting_data.xlsx [IDEA, early voting arrangements]
> 
>         Download data at:
>         https://www.idea.int/data-tools/tools/special-voting-arrangements/postal-in-country 
>                 [accessed: November 2023]
>         
> Task: compute stats on prevalence of early voting and postal voting arrangements 
>                 across countries
> 
> */      
.         
. * READ: postal voting stats, IDEA
. import excel  "$rawdata/IDEA/cleaned_IDEA_postal_voting_data.xlsx", clear firstrow
(9 vars, 204 obs)

.         
.         ren Country                             country

.         ren Available                   postal 

.         ren Whoiseligible               ps_who

.         ren Whatisthetimeframe  ps_time

.         ren  LawSources                 ps_law

.         ren Notes                               ps_notes

.         
.         encoder democracy, replace

.         lab var democracy "Economist Intelligence Unit Demo class"

.         
.         gen     ipostal = (postal=="Yes; all voters")

.         lab var ipostal "=1 if postal v avlb. to all voters"

. 
.         
.         tempfile postal

.         save `postal'
file C:\Users\Alipour\AppData\Local\Temp\9\ST_77c8_000001.tmp saved as .dta format

.         
. * READ: early voting stats, IDEA
. import excel  "$rawdata/IDEA/cleaned_IDEA_early_voting_data.xlsx", clear firstrow
(6 vars, 204 obs)

.         
.         ren Country                             country

.         ren Available                   early

.         ren Whoiseligible               ev_who

.         ren Whatisthetimeframe  ev_time

.         ren  LawSource          ev_law

.         ren Notes                               ev_notes

.         
.         gen     iearly = (early=="Yes; all voters")

.         lab var iearly "=1 if early v avlb. to all voters"      

.         
.         merge 1:1 country using `postal', assert(3) nogen

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               204  
    -----------------------------------------

.         
.         
.         // either postal or early 
.         gen either = (ipostal==1|iearly==1)

.         
. ** Stats: 204 countries, 28 OECD, 27 EU
.  
. // global 
.  fre postal     // 5.9% universal

postal -- Available?
----------------------------------------------------------------------
                         |      Freq.    Percent      Valid       Cum.
-------------------------+--------------------------------------------
Valid   No               |        171      83.82      83.82      83.82
        Yes; all voters  |         12       5.88       5.88      89.71
        Yes; some voters |         21      10.29      10.29     100.00
        Total            |        204     100.00     100.00           
----------------------------------------------------------------------

.  fre early              // 7.8% universal

early -- Available?
----------------------------------------------------------------------
                         |      Freq.    Percent      Valid       Cum.
-------------------------+--------------------------------------------
Valid   No               |        126      61.76      61.76      61.76
        Yes; all voters  |         16       7.84       7.84      69.61
        Yes; some voters |         62      30.39      30.39     100.00
        Total            |        204     100.00     100.00           
----------------------------------------------------------------------

.  
. //oecd
. fre ipostal if oecd==1          // 29% universal 

ipostal -- =1 if postal v avlb. to all voters
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   0     |         27      71.05      71.05      71.05
        1     |         11      28.95      28.95     100.00
        Total |         38     100.00     100.00           
-----------------------------------------------------------

. fre iearly if oecd==1           // 34%

iearly -- =1 if early v avlb. to all voters
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   0     |         25      65.79      65.79      65.79
        1     |         13      34.21      34.21     100.00
        Total |         38     100.00     100.00           
-----------------------------------------------------------

. 
. //eu
. fre ipostal if eu==1    // 14.8% universal 

ipostal -- =1 if postal v avlb. to all voters
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   0     |         23      85.19      85.19      85.19
        1     |          4      14.81      14.81     100.00
        Total |         27     100.00     100.00           
-----------------------------------------------------------

. fre iearly if eu==1             // 25.9% universal

iearly -- =1 if early v avlb. to all voters
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   0     |         20      74.07      74.07      74.07
        1     |          7      25.93      25.93     100.00
        Total |         27     100.00     100.00           
-----------------------------------------------------------

. 
. tab either                              // 11.3% 

     either |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        181       88.73       88.73
          1 |         23       11.27      100.00
------------+-----------------------------------
      Total |        204      100.00

. tab either if oecd==1           // 50% 

     either |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         19       50.00       50.00
          1 |         19       50.00      100.00
------------+-----------------------------------
      Total |         38      100.00

. tab either if eu==1                     // 37%

     either |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         17       62.96       62.96
          1 |         10       37.04      100.00
------------+-----------------------------------
      Total |         27      100.00

. 
end of do-file
Running: 01e_descriptives_representativeness_munich_table_e1.do

. /*
> 
> Input: corplat_kreise_yrl.dta [county-level stats from Corona-Datenplattform]
>                 > Raw data avlb at: https://www.healthcare-datenplattform.de/ [accessed: June 23
> ]
> 
> Output: Table E.1
>                 
> Tasks: Compare Munich to top 20 counties across sociodem characteristics
> 
> */
. 
. *--- PULL Corona datenplattform, county-level data, 2019
. use "$rawdata/CoronaDatenplattform/corplat_kreise_yrl.dta", clear

. 
. // id for 19 biggest cities in GER + Munich
. gen id = inlist(ags5,9162, 11000, 2000, 5315, 6412, 8111, 5111, 14713, 5913, 5113, 4011, 14612, 
> 3241, 9564, 5112,5911,5124,5711,5314,5515)

. replace id=. if id==0
(381 real changes made, 381 to missing)

. 
. 
. ** Genvars
. gen     shr_65plus = (kr_ew_65u75+ kr_ew_75)/kr_ew_19

. 
. gen     shr_wa_pop =(kr_ew_15u18+ kr_ew_18u20 +kr_ew_20u25 +kr_ew_25u30 +kr_ew_30u35 ///
>                                         +kr_ew_35u40 +kr_ew_40u45 +kr_ew_45u50 +kr_ew_50u55 +kr_
> ew_55u60 +kr_ew_60u65) / kr_ew_19 

. 
. gen     shr_abi = kr_schul_hoch/kr_ew_19

. 
. replace kr_selbst =. if kr_selbst<0
(56 real changes made, 56 to missing)

. gen     shr_selbst = kr_selbst/kr_erwt_ao
(56 missing values generated)

. 
. gen     shr_uni = (kr_schul_hoch +kr_beruf_hochschul)/kr_ew_19

. 
. gen     shr_foreign = kr_ausl_anz/kr_ew_19

. 
. gen     pop1000 = kr_ew_19/1000

. 
. lab var pop1000         "Population (in thsd)"

. lab var shr_65plus  "\% Population Aged 65+"

. lab var shr_wa_pop  "\% Working Age Population (15-65)"

. lab var kr_ein_md       "Median Income (in euros)"

. lab var kr_btw_ant      "Turnout, Federal Election 2017 (in \%)"

. lab var shr_selbst  "\% Self Employed"

. lab var shr_uni         "\% College Educated"

. lab var shr_foreign "\% Foreigners"

. lab var kr_bsdl_dichte "Population density (in inhab. per sqkm)"

. 
. * Create Table
. 
. // variables
. global vars pop1000 kr_bsdl_dichte shr_65plus shr_wa_pop shr_uni shr_foreign shr_selbst kr_ein_m
> d kr_btw_ant 

. 
. foreach v in $vars {
  2.         gsort id -`v'
  3.         gen rk_`v' = _n if id!=.
  4. }
(381 missing values generated)
(381 missing values generated)
(381 missing values generated)
(381 missing values generated)
(381 missing values generated)
(381 missing values generated)
(381 missing values generated)
(381 missing values generated)
(381 missing values generated)

. 
. 
. tabstat $vars  if id!=., stat(min mean max) format(%12.2f)

   Stats |      pop1000     kr_bsd~e     shr_65~s     shr_wa~p      shr_uni     shr_fo~n
---------+------------------------------------------------------------------------------
     Min |       315.29      2626.00         0.15         0.63         0.30         0.08
    Mean |       843.63      4426.80         0.19         0.67         0.55         0.18
     Max |      3669.49      6439.00         0.22         0.70         0.75         0.29
----------------------------------------------------------------------------------------

   Stats |     shr_se~t     kr_ein~d     kr_bt~nt
---------+---------------------------------------
     Min |         0.06      2807.00        68.70
    Mean |         0.08      3530.25        75.77
     Max |         0.12      4351.00        82.30
-------------------------------------------------

. tabstat $vars if ags5==9162,  format(%12.2f)

   Stats |      pop1000     kr_bsd~e     shr_65~s     shr_wa~p      shr_uni     shr_fo~n
---------+------------------------------------------------------------------------------
    Mean |      1484.23      6439.00         0.17         0.68         0.69         0.26
----------------------------------------------------------------------------------------

   Stats |     shr_se~t     kr_ein~d     kr_bt~nt
---------+---------------------------------------
    Mean |         0.10      4169.00        78.50
-------------------------------------------------

. 
.   
.         * TABLE E1. Representativeness of Munich
.         eststo clear

.         eststo m_ger: estpost tabstat $vars, statistics(min max mean) col(statistics)

Summary statistics: min max mean
     for variables: pop1000 kr_bsdl_dichte shr_65plus shr_wa_pop shr_uni shr_foreign shr_selbst kr
> _ein_md kr_btw_ant

             |    e(min)     e(max)    e(mean) 
-------------+---------------------------------
     pop1000 |    34.193   3669.491   207.3983 
kr_bsdl_di~e |       612       6439   2169.716 
  shr_65plus |  .1542613   .3183027   .2227967 
  shr_wa_pop |  .5436952   .7105675   .6424534 
     shr_uni |  .2219644   .7633898   .3699937 
 shr_foreign |  .0197404   .3543138   .1046205 
  shr_selbst |  .0254373   .3897445   .1006331 
   kr_ein_md |      2183       4635   3064.945 
  kr_btw_ant |      64.1       84.4    75.8414 

.         eststo m_top: estpost tabstat $vars if id!=., statistics(min max mean) col(statistics)

Summary statistics: min max mean
     for variables: pop1000 kr_bsdl_dichte shr_65plus shr_wa_pop shr_uni shr_foreign shr_selbst kr
> _ein_md kr_btw_ant

             |    e(min)     e(max)    e(mean) 
-------------+---------------------------------
     pop1000 |   315.293   3669.491   843.6257 
kr_bsdl_di~e |      2626       6439     4426.8 
  shr_65plus |  .1542613    .220202   .1943501 
  shr_wa_pop |  .6333525   .7011605   .6653519 
     shr_uni |  .3048411   .7543523   .5457104 
 shr_foreign |  .0753817   .2890749   .1767425 
  shr_selbst |   .057826   .1199136   .0804572 
   kr_ein_md |      2807       4351    3530.25 
  kr_btw_ant |      68.7       82.3     75.765 

.         eststo m_muc: estpost tabstat $vars if ags5==9162, statistics(mean) col(statistics)

Summary statistics: mean
     for variables: pop1000 kr_bsdl_dichte shr_65plus shr_wa_pop shr_uni shr_foreign shr_selbst kr
> _ein_md kr_btw_ant

             |   e(mean) 
-------------+-----------
     pop1000 |  1484.226 
kr_bsdl_di~e |      6439 
  shr_65plus |  .1746971 
  shr_wa_pop |    .68226 
     shr_uni |  .6884773 
 shr_foreign |  .2575302 
  shr_selbst |  .0981349 
   kr_ein_md |      4169 
  kr_btw_ant |      78.5 

.         // replace variable with MUC rank value
.         foreach v in $vars {
  2.                 replace `v'=rk_`v'
  3.         }       
(401 real changes made, 381 to missing)
(401 real changes made, 381 to missing)
(401 real changes made, 381 to missing)
(401 real changes made, 381 to missing)
(401 real changes made, 381 to missing)
(401 real changes made, 381 to missing)
(347 real changes made, 327 to missing)
(401 real changes made, 381 to missing)
(401 real changes made, 381 to missing)

.         eststo m_mucrk: estpost tabstat $vars if ags5==9162, statistics(mean) col(statistics)

Summary statistics: mean
     for variables: pop1000 kr_bsdl_dichte shr_65plus shr_wa_pop shr_uni shr_foreign shr_selbst kr
> _ein_md kr_btw_ant

             |   e(mean) 
-------------+-----------
     pop1000 |         3 
kr_bsdl_di~e |         1 
  shr_65plus |        17 
  shr_wa_pop |         5 
     shr_uni |         3 
 shr_foreign |         2 
  shr_selbst |         2 
   kr_ein_md |         3 
  kr_btw_ant |         5 

.         esttab m_ger m_top m_muc m_mucrk using "$tables/Table_E1_muc_represent.tex", replace lab
> el substitute(# \#) ///
>                 fragment nonumber  noobs type ///
>                 mtitle("Germany" "Top 20" "Munich" "") cells(" min(label(Min) fmt(2)) mean(label
> (Mean) fmt(2)) max(label(Max) fmt(2))")
(file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_E1_muc_r
    > epresent.tex not found)

                    &\multicolumn{3}{c}{Germany}           &\multicolumn{3}{c}{Top 20}            
> &\multicolumn{3}{c}{Munich}            &\multicolumn{3}{c}{}                  \\
                    &         Min&        Mean&         Max&         Min&        Mean&         Max
> &         Min&        Mean&         Max&         Min&        Mean&         Max\\
\hline
Population (in thsd)&       34.19&      207.40&     3669.49&      315.29&      843.63&     3669.49
> &            &     1484.23&            &            &        3.00&            \\
Population density (in inhab. per sqkm)&      612.00&     2169.72&     6439.00&     2626.00&     4
> 426.80&     6439.00&            &     6439.00&            &            &        1.00&           
>  \\
\% Population Aged 65+&        0.15&        0.22&        0.32&        0.15&        0.19&        0.
> 22&            &        0.17&            &            &       17.00&            \\
\% Working Age Population (15-65)&        0.54&        0.64&        0.71&        0.63&        0.67
> &        0.70&            &        0.68&            &            &        5.00&            \\
\% College Educated &        0.22&        0.37&        0.76&        0.30&        0.55&        0.75
> &            &        0.69&            &            &        3.00&            \\
\% Foreigners       &        0.02&        0.10&        0.35&        0.08&        0.18&        0.29
> &            &        0.26&            &            &        2.00&            \\
\% Self Employed    &        0.03&        0.10&        0.39&        0.06&        0.08&        0.12
> &            &        0.10&            &            &        2.00&            \\
Median Income (in euros)&     2183.00&     3064.95&     4635.00&     2807.00&     3530.25&     435
> 1.00&            &     4169.00&            &            &        3.00&            \\
Turnout, Federal Election 2017 (in \%)&       64.10&       75.84&       84.40&       68.70&       
> 75.77&       82.30&            &       78.50&            &            &        5.00&            
> \\
(output written to //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/T
> able_E1_muc_represent.tex)

. 
end of do-file
Running: 02_sumstats_and_balance_figure_6_tables_e2_e3_e4.do

. /*
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> 
> Output: 
>         - Table E.2, E.3, E.4
>         - Figure 6
>         
> Tasks:
>         - Summary Statistics Table
>         - Balancing exercise: Correlation of reassignment timing and changes in 
>                 precinct characteristics, standardized and nonstandardized characteristics
> 
> 
> */      
.         
. * PULL: Stimmbezirk-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
.         
. ********************************************************************************
. *        Prep *
. ********************************************************************************
.         
.         xtset sb_new wahl_id

Panel variable: sb_new (strongly balanced)
 Time variable: wahl_id, 1 to 8
         Delta: 1 unit

.         
.         // Relabel Outcomes
.         lab var turnout_urne    "Polling Place Turnout"

.         lab var turnout_pos_req "Mail-in Turnout (Requested Polling Cards)"

.         lab var turnout_tot_req "Total Turnout"

.         
.         //Relabel variables of interest
.         lab var street_dist     "Avg. Walking Distance to the Polling Place (in km)"

.         lab var treat_simple    "Share of Reassigned Residential Addresses"

.         lab var treat_consol    "Share Reassigned (Precinct Reconfiguration)"

.         lab var treat_no_consol "Share Reassigned (Recruitment of Polling Location)"

.         
.         // Relabel Precinct Characteristics for Summary Table 
.         lab var ew_ges          "# residents"

.         lab var wb_anteil       "\% Residents Eligible to Vote"

.         lab var ew_dtmihi       "\% Non-native German Residents"

.         lab var ew_biodt        "\% Native German Residents"

.         lab var ew_ausl_eu      "\% EU Foreigners"

.         lab var ew_ausl_else "\% Non-EU Foreigners"

.         lab var ew_ledig        "\% Single Residents"

.         lab var ew_married      "\% Married Residents"

.         lab var wb_18t24        "\% Electorate Aged 18-24"

.         lab var wb_25t34        "\% Electorate Aged 25-34"

.         lab var wb_35t44        "\% Electorate Aged 35-44"

.         lab var wb_45t59        "\% Electorate Aged 45-59"

.         lab var wb_60plus       "\% Electorate Aged 60+"

.         lab var wb_ausl         "\% EU Foreigners in the Electorate"

.         lab var hh_kids         "\% Households with Children"

.         lab var avg_dur         "Avg. Duration of Residence"

.         lab var mpreis_flats_rent "Avg. Quoted Rent per sqm"

. 
.         // Relabel Precinct Characteristics for Balance Table 
.         lab var abs_ew_ges              "# Residents"

.         lab var abs_ew_ledig    "# Single Residents"

.         lab var abs_ew_married  "# Married Residents"

.         lab var abs_ew_biodt    "# Native German Residents"

.         lab var abs_ew_dtmihi   "# Non-native German Residents"

.         lab var abs_ew_ausl     "# Foreign Residents"

.         lab var abs_wb_anteil   "# Eligible Voters"

.         lab var abs_wb_18t24    "# Eligible Voters Aged 18-24"

.         lab var abs_wb_25t34    "# Eligible Voters Aged 25-34"

.         lab var abs_wb_35t44    "# Eligible Voters Aged 35-44"

.         lab var abs_wb_45t59    "# Eligible Voters Aged 45-59"

.         lab var abs_wb_60plus   "# Eligible Voters Aged 60+"

.         lab var abs_wb_dt               "# German Eligible Voters"

.         lab var abs_wb_ausl     "# EU Foreigners in the Electorate"

.         lab var hh_kids                 "\% Households with Children"

.         lab var mpreis_flats_rent "Avg. Quoted Rent per sqm"

.         lab var avg_dur                 "Avg. Duration of Residence"

.         lab var withmig                 "# Within Migration"

.         lab var outmig                  "# Outmigration"

.         lab var inmig                   "# Inmigration"

.         
. ********************************************************************************
. *       Summary Statistics *
. ********************************************************************************
. * Summary Statistics: Precinct (converted SE-18)
.         local outcomes  "turnout_urne turnout_pos_req turnout_tot_req"

.         local varint    "street_dist treat_simple treat_consol treat_no_consol"

.         local sum_ctr   "ew_ges wb_anteil ew_dtmihi ew_biodt ew_ausl_eu ew_ausl_else ew_ledig ew
> _married wb_18t24 wb_25t34 wb_35t44 wb_45t59 wb_60plus wb_ausl hh_kids avg_dur mpreis_flats_rent
> "

.                 
.         *change labels for non-standardized variables
.                 lab var mpreis_flats_rent       "Avg. Quoted Rent per sqm (in euros)"

.                 lab var avg_dur                         "Avg. Duration of Residence (in years)"

.         
.         * TABLE E2. Summary Statistics of Precinct Characteristics
.         eststo clear

.         estpost summarize `outcomes' `varint' `sum_ctr', det

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~) 
-------------+-----------------------------------------------------------------------------
turnout_urne |      4944       4944   34.23533   81.70719   9.039203  -.1435642   1.945217 
turnout_po~q |      4944       4944   28.91842   58.42142   7.643391  -.1370892   2.436906 
turnout_to~q |      4944       4944   63.15376   212.1961   14.56695    -.32107   2.160241 
 street_dist |      4944       4944   .7136388   .1162883   .3410107   1.276468   5.262301 
treat_simple |      4944       4944   .1379298   .0999807   .3161972   2.120111   5.785119 
treat_consol |      4944       4944   .0539769   .0361635    .190167   3.969305   18.18019 
treat_no_c~l |      4944       4944    .083953   .0698199   .2642345   2.993976   10.19974 
      ew_ges |      4944       4944   2428.043   162705.7    403.368   1.695203   9.016213 
   wb_anteil |      4944       4944   65.34833   83.69997   9.148769  -.6224924   3.319466 
   ew_dtmihi |      4944       4944     14.676   18.91551   4.349196   1.606175    6.11443 
    ew_biodt |      4944       4944   59.76861    128.871   11.35214   -.626565   2.941059 
  ew_ausl_eu |      4944       4944   12.89752   15.79541   3.974345   1.071235   5.377232 
ew_ausl_else |      4944       4944    12.6583     38.247   6.184416   1.028043   4.507875 
    ew_ledig |      4944       4944   49.72506   53.82692   7.336683   .4964683   2.755009 
  ew_married |      4944       4944   37.28863   42.15676   6.492824  -.1697355    2.24541 
    wb_18t24 |      4944       4944   8.738279    8.25379   2.872941   4.096262   36.11122 
    wb_25t34 |      4944       4944   21.14891   43.22711   6.574733   .3457753   2.525164 
    wb_35t44 |      4944       4944   17.92067   16.00132   4.000165   .7318986   3.973384 
    wb_45t59 |      4944       4944   24.62122    15.7978   3.974645   .1954052   3.661227 
   wb_60plus |      4944       4944   27.56952   70.31077   8.385151   .2170427   2.914649 
     wb_ausl |      4944       4944   8.290401   83.26681   9.125065   .5902627   2.281812 
     hh_kids |      4944       4944   17.52943   36.95805   6.079314   1.640908    8.72906 
     avg_dur |      4944       4944   21.69366   19.76598   4.445895   .4017726   4.368049 
mpreis_fla~t |      4944       4944   17.42224   20.65468   4.544742   .9570932   3.771824 

             |    e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25) 
-------------+-----------------------------------------------------------------------------
turnout_urne |  169259.5   9.938712   55.85818   16.30773   19.85452    22.0808    26.1821 
turnout_po~q |  142972.7   4.014273   51.98697   12.18859   16.29457   18.62152   23.09896 
turnout_to~q |  312232.2   15.10321   91.71677     30.641   38.88374   43.03911   51.19595 
 street_dist |   3528.23   .1561487   2.558443   .2195329   .3078175   .3418702    .472811 
treat_simple |  681.9252          0          1          0          0          0          0 
treat_consol |  266.8617          0          1          0          0          0          0 
treat_no_c~l |  415.0635          0          1          0          0          0          0 
      ew_ges |  1.20e+07    757.914       6272    1857.35       1990       2062       2169 
   wb_anteil |  323082.1   24.61735   86.92683   40.21736   48.07627   52.36146   60.22421 
   ew_dtmihi |  72558.12   5.500002   35.78023          9   10.00802   10.58555   11.70135 
    ew_biodt |    295496         21   83.97144   30.45182   38.28853   42.90687   52.75354 
  ew_ausl_eu |  63765.35          4   36.04938   5.865768   7.538901   8.504399    10.1307 
ew_ausl_else |  62582.65   1.914795   50.81719        3.4   4.912223   5.812171   7.971276 
    ew_ledig |  245840.7   35.28311       80.2   37.50525   39.51303   40.92923   43.72155 
  ew_married |    184355       15.5   51.84211   23.05867    26.8315   28.66829    32.2835 
    wb_18t24 |  43202.05   2.409638   49.06717        4.7        5.7   6.288268        7.2 
    wb_25t34 |  104560.2        7.4       42.3   9.713533       11.5   12.87783   15.72702 
    wb_35t44 |  88599.79        6.3       34.7       10.3   12.23024   13.40772   15.23195 
    wb_45t59 |  121727.3   4.850746   45.31835       15.8       18.4   19.77438    21.9728 
   wb_60plus |  136303.7    2.61194       63.8       10.8       14.4   16.51786       21.3 
     wb_ausl |  40987.74          0   46.38876          0          0          0          0 
     hh_kids |  86665.48   5.313745   58.74811        7.8   9.865183   11.10633   13.35391 
     avg_dur |  107253.4        6.8   45.10922       12.1       14.9   16.22601   18.52521 
mpreis_fla~t |  86135.54   6.692658    43.9157   11.38759   12.18011   12.63516   13.67402 

             |    e(p50)     e(p75)     e(p90)     e(p95)     e(p99) 
-------------+-------------------------------------------------------
turnout_urne |  35.53579    41.7041   45.49347   47.50733   50.88409 
turnout_po~q |  29.46233   34.70147   38.56254    40.6705   44.35897 
turnout_to~q |  65.27287    75.2642   81.11111   84.06847    87.1959 
 street_dist |  .6360515   .8718103   1.186377   1.387347   1.740753 
treat_simple |         0          0   .8953488          1          1 
treat_consol |         0          0       .096   .4431818          1 
treat_no_c~l |         0          0   .1219512          1          1 
      ew_ges |   2324.62   2590.744       2931   3191.061   3840.198 
   wb_anteil |  66.42257   71.69535   76.50546   78.77008   81.92712 
   ew_dtmihi |  13.47564   16.45438   20.56437   23.35105   30.62773 
    ew_biodt |      61.8   68.10501   72.68876   75.31384   80.00423 
  ew_ausl_eu |   12.3846   14.99343   17.90126   20.11473   25.40918 
ew_ausl_else |  11.48739   16.06434   21.44021       24.7   30.45959 
    ew_ledig |  48.83575   55.02317    60.0267   62.25667   67.97511 
  ew_married |  37.42873   42.76984   45.56238   46.83651   49.55791 
    wb_18t24 |  8.249514   9.643708   11.32019         13   18.79958 
    wb_25t34 |  20.83451   26.00938   29.66667   32.48945   37.27554 
    wb_35t44 |  17.37116   20.08456   23.25522   25.25553   30.10381 
    wb_45t59 |      24.4    27.2493   29.53995   31.12456   34.92132 
   wb_60plus |  27.57309   33.28689   38.32091   41.45319   47.22154 
     wb_ausl |       2.7   15.81047   20.25948   23.52325   30.18868 
     hh_kids |  16.69108   20.43092   24.27833   27.39593   39.42808 
     avg_dur |  21.72248   24.51183   26.96952   28.78263   33.58828 
mpreis_fla~t |  16.45299   20.29763   24.13921   25.95291   30.19621 

.         esttab using "${tables}/Table_E2_sumstat.tex", replace label substitute(# \#) ///
>                 fragment nomtitle nonumber noobs ///
>                         cells("mean(label(Mean) fmt(2)) sd(label(Std. Dev.) fmt(2)) min(label(Mi
> n) fmt(2)) p25(fmt(2)) p50(label(Median) fmt(2)) p75(fmt(2)) max(label(Max) fmt(2))")
(file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_E2_sumst
    > at.tex not found)
(output written to //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/T
> able_E2_sumstat.tex)

. 
.         
. ********************************************************************************
. * Is reassignment timing corr w/ changes in precinct characteristics? *
. ********************************************************************************
. 
. * Univariate regressions of treatment on precinct characteristics (+ FEs) + check for joint sign
> ificance
.         * Approach: "stack" regressions on top of each other (i.e. duplicate data + include inte
> racted FE)
.         * Standardize (unit SD, mean zero) INDEP variables for better exposition
.         * Resulting OLS coefs are EQUIVALENT to separate univariate regressions
.         * But in this way, we can directly perform an F-test of joint significance
.         
.         
.         // variables on which to check balance
.         global bal_test abs_ew_ges abs_ew_ledig abs_ew_married abs_ew_biodt abs_ew_dtmihi abs_ew
> _ausl abs_wb_anteil ///
>                                         abs_wb_18t24 abs_wb_25t34 abs_wb_35t44 abs_wb_45t59 abs_
> wb_60plus abs_wb_dt abs_wb_ausl withmig outmig inmig hh_kids mpreis_flats_rent avg_dur  

.         
.         *change labels before standardization for figure output
.                 lab var mpreis_flats_rent       "Avg. Quoted Rent per sqm"

.                 lab var avg_dur                         "Avg. Duration of Residence"

.                                         
.         // z-scores for bal_test variables: gen zs_*
.         cap drop zs_*

.         foreach v of varlist $bal_test {
  2.                 qui su `v'
  3.                 gen zs_`v' = (`v'-r(mean))/r(sd)
  4.                 local lb: variable lab `v'
  5.                 local lb_new=ustrregexra("`lb'","\(thsd\)","")
  6.                 local lb_new=ustrregexra("`lb_new'","\\","")
  7.                 lab var zs_`v' "`lb_new'"
  8.         }

.                                                 
.         
. cap     frame copy      default ftest, replace

. frame   ftest {
.         
.         ** i) Prep
.                 // label outcomes for tables
.                 lab var parttreat_simple        "\makecell{Indicator \\ (Reassigned >0)}"
.                 lab var fulltreat_simple        "\makecell{Indicator \\ (Reassigned =100)}"     
>         
.                 lab var treat_simple            "\makecell{Share \\ Reassigned}"
.                 lab var treat_no_consol         "\makecell{Share Reassigned \\ (Recruitment)}"  
>         
.                 lab var treat_consol            "\makecell{Share Reassigned \\ (Precinct Reconfi
> g.)}"
.                 lab var ln_street_dist          "\makecell{Log \\ Avg. Walking Distance}"
.                 
.                 // prep data: duplicate dataset + gen 'dup': dataset_id for FE later
.                 gen id = _n
.                 local num : list sizeof global(bal_test)                // extract number of Cov
> ariates
.                 expand `num'                                                                    
> // expand dataset by number of Covariates
(93,936 observations created)
.                 bys id: gen dup = _n                                                    // gen d
> up: id for each duplicate dataset
.                 tab dup, gen(d)                                                                 
> // gen dummies for number of identical "datasets"

        dup |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      4,944        5.00        5.00
          2 |      4,944        5.00       10.00
          3 |      4,944        5.00       15.00
          4 |      4,944        5.00       20.00
          5 |      4,944        5.00       25.00
          6 |      4,944        5.00       30.00
          7 |      4,944        5.00       35.00
          8 |      4,944        5.00       40.00
          9 |      4,944        5.00       45.00
         10 |      4,944        5.00       50.00
         11 |      4,944        5.00       55.00
         12 |      4,944        5.00       60.00
         13 |      4,944        5.00       65.00
         14 |      4,944        5.00       70.00
         15 |      4,944        5.00       75.00
         16 |      4,944        5.00       80.00
         17 |      4,944        5.00       85.00
         18 |      4,944        5.00       90.00
         19 |      4,944        5.00       95.00
         20 |      4,944        5.00      100.00
------------+-----------------------------------
      Total |     98,880      100.00
.                 
.                 // gen interaction terms with STANDARDIZED cov: covariate x dataset_id
.                 local j = 0
.                 foreach v of varlist /*$bal_test*/ zs_* {                       // < z-scores OR
>  abs values
  2.                         local j = `j' + 1
  3.                         clonevar        szint_`v' = `v'
  4.                         replace         szint_`v' = d`j' * `v'
  5.                 }
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
.                 
.                 // gen interaction terms with NON-stdz cov: covariate x dataset_id
.                 local j = 0
.                 foreach v of varlist $bal_test /*zs_*/ {                        // < z-scores OR
>  abs values
  2.                         local j = `j' + 1
  3.                         clonevar        int_`v' = `v'
  4.                         replace         int_`v' = d`j' * `v'
  5.                 }
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(46,968 real changes made)
(91,143 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
(93,936 real changes made)
.                 
.                 *change labels for non-standardized case for figure
.                         lab var int_mpreis_flats_rent "Avg. Quoted Rent per sqm (euros)"
.                         lab var int_avg_dur                     "Avg. Duration of Residence (yea
> rs)"
.           
.         ** iia) Run regression with interactions using STDZD covariates
.         * Note: OLS coefs equivalent to separate univariate regs
.         * 6 Outomes: Dummy(full precinct reass), Dummy(at least part reass), %addresses reass, %
>  addresses reasigned by reason, log walking dist
.                 outreg, clear
.                 estimates clear
.                 foreach depvar of varlist fulltreat_simple parttreat_simple treat_simple  treat_
> consol treat_no_consol ln_street_dist {
  2.                          reghdfe  `depvar' szint_* $wgt, absorb(i.dup#i.wahl_id i.dup#i.sb_ne
> w)  vce(cluster sb_new)
  3.                         
.                         // save coefs 
.                         estimates store `depvar'_zs
  4.                         
.                         // run F-test: b1 = b2 = ... = 0
.                         local test ""
  5.                         foreach v of varlist szint_*{   
  6.                                 local test "`test' _b[`v'] = "
  7.                         }
  8.                         qui test "`test'" 0
  9.                         
.                         // create table
.                         local obs = `e(N)' / `num' 
 10.                         qui outreg, $opt3 noautosumm addrow(Observations, `obs' \"\$ F\$-test
>  on joint insignificance [$ Pr>F$]", "`:di %6.2f `r(F)'' [`:di %6.2f `r(p)'']" \ ///
>                                                         Precinct FE, X \ Election FE, X) 
 11.                 }
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.54
Statistics robust to heteroskedasticity           Prob > F        =     0.9513
                                                  R-squared       =     0.2691
                                                  Adj R-squared   =     0.1630
                                                  Within R-sq.    =     0.0003
Number of clusters (sb_new)  =        618         Root MSE        =     0.2606

                                             (Std. err. adjusted for 618 clusters in sb_new)
--------------------------------------------------------------------------------------------
                           |               Robust
          fulltreat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       szint_zs_abs_ew_ges |   .0152886   .0137747     1.11   0.267    -.0117624    .0423397
     szint_zs_abs_ew_ledig |    .017197   .0165335     1.04   0.299    -.0152716    .0496657
   szint_zs_abs_ew_married |    .007455    .016137     0.46   0.644    -.0242351     .039145
     szint_zs_abs_ew_biodt |   .0065623   .0100943     0.65   0.516    -.0132611    .0263857
    szint_zs_abs_ew_dtmihi |   .0207629   .0201827     1.03   0.304    -.0188722    .0603979
      szint_zs_abs_ew_ausl |   .0162374   .0194501     0.83   0.404     -.021959    .0544338
    szint_zs_abs_wb_anteil |   .0089458   .0126156     0.71   0.479    -.0158289    .0337206
     szint_zs_abs_wb_18t24 |   .0096351   .0096167     1.00   0.317    -.0092503    .0285204
     szint_zs_abs_wb_25t34 |   .0051325   .0123708     0.41   0.678    -.0191614    .0294265
     szint_zs_abs_wb_35t44 |  -.0033408   .0088774    -0.38   0.707    -.0207744    .0140928
     szint_zs_abs_wb_45t59 |   .0150794   .0104805     1.44   0.151    -.0055023    .0356611
    szint_zs_abs_wb_60plus |    .008634   .0131234     0.66   0.511    -.0171379    .0344059
        szint_zs_abs_wb_dt |   .0122269   .0087001     1.41   0.160    -.0048585    .0293123
      szint_zs_abs_wb_ausl |   .0019318    .010487     0.18   0.854    -.0186628    .0225264
          szint_zs_withmig |   -.003172   .0060558    -0.52   0.601    -.0150644    .0087205
           szint_zs_outmig |   .0112089   .0122012     0.92   0.359     -.012752    .0351698
            szint_zs_inmig |   .0036681   .0100111     0.37   0.714    -.0159919    .0233281
          szint_zs_hh_kids |  -.0027265    .021413    -0.13   0.899    -.0447777    .0393248
szint_zs_mpreis_flats_rent |   .0147588   .0119078     1.24   0.216    -.0086259    .0381435
          szint_zs_avg_dur |   .0006555   .0106924     0.06   0.951    -.0203425    .0216534
                     _cons |   .0888316   .0002247   395.25   0.000     .0883902    .0892729
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.70
Statistics robust to heteroskedasticity           Prob > F        =     0.8316
                                                  R-squared       =     0.5039
                                                  Adj R-squared   =     0.4318
                                                  Within R-sq.    =     0.0001
Number of clusters (sb_new)  =        618         Root MSE        =     0.3141

                                             (Std. err. adjusted for 618 clusters in sb_new)
--------------------------------------------------------------------------------------------
                           |               Robust
          parttreat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       szint_zs_abs_ew_ges |  -.0023141   .0178044    -0.13   0.897    -.0372786    .0326504
     szint_zs_abs_ew_ledig |   .0091146    .020362     0.45   0.655    -.0308726    .0491018
   szint_zs_abs_ew_married |  -.0194595   .0236344    -0.82   0.411    -.0658732    .0269542
     szint_zs_abs_ew_biodt |  -.0169931   .0146444    -1.16   0.246     -.045752    .0117658
    szint_zs_abs_ew_dtmihi |  -.0099597    .028202    -0.35   0.724    -.0653432    .0454239
      szint_zs_abs_ew_ausl |   .0155156   .0201014     0.77   0.440    -.0239598    .0549909
    szint_zs_abs_wb_anteil |  -.0061529   .0156785    -0.39   0.695    -.0369426    .0246369
     szint_zs_abs_wb_18t24 |   .0005551   .0120272     0.05   0.963    -.0230642    .0241744
     szint_zs_abs_wb_25t34 |   .0120576   .0151094     0.80   0.425    -.0176145    .0417296
     szint_zs_abs_wb_35t44 |  -.0064231   .0116697    -0.55   0.582    -.0293402    .0164941
     szint_zs_abs_wb_45t59 |  -.0141811   .0127458    -1.11   0.266    -.0392115    .0108492
    szint_zs_abs_wb_60plus |  -.0120785   .0150771    -0.80   0.423    -.0416871    .0175301
        szint_zs_abs_wb_dt |  -.0063046   .0117273    -0.54   0.591    -.0293348    .0167256
      szint_zs_abs_wb_ausl |  -.0038203   .0145475    -0.26   0.793    -.0323889    .0247483
          szint_zs_withmig |   .0005959   .0083647     0.07   0.943    -.0158309    .0170227
           szint_zs_outmig |   .0120366   .0118146     1.02   0.309    -.0111652    .0352384
            szint_zs_inmig |    .004269   .0163554     0.26   0.794      -.02785     .036388
          szint_zs_hh_kids |  -.0015316    .024894    -0.06   0.951    -.0504189    .0473557
szint_zs_mpreis_flats_rent |  -.0050292   .0147282    -0.34   0.733    -.0339527    .0238943
          szint_zs_avg_dur |  -.0057733   .0137308    -0.42   0.674    -.0327382    .0211916
                     _cons |   .2237342   .0003129   714.94   0.000     .2231196    .2243487
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.59
Statistics robust to heteroskedasticity           Prob > F        =     0.9227
                                                  R-squared       =     0.3975
                                                  Adj R-squared   =     0.3100
                                                  Within R-sq.    =     0.0004
Number of clusters (sb_new)  =        618         Root MSE        =     0.2645

                                             (Std. err. adjusted for 618 clusters in sb_new)
--------------------------------------------------------------------------------------------
                           |               Robust
              treat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       szint_zs_abs_ew_ges |   .0226126   .0139244     1.62   0.105    -.0047325    .0499577
     szint_zs_abs_ew_ledig |   .0306454   .0164121     1.87   0.062    -.0015849    .0628757
   szint_zs_abs_ew_married |   .0148693   .0178574     0.83   0.405    -.0201994     .049938
     szint_zs_abs_ew_biodt |   .0066489   .0116889     0.57   0.570    -.0163059    .0296037
    szint_zs_abs_ew_dtmihi |   .0284622   .0210955     1.35   0.178    -.0129655    .0698899
      szint_zs_abs_ew_ausl |   .0278052    .018033     1.54   0.124    -.0076083    .0632187
    szint_zs_abs_wb_anteil |   .0085049   .0120522     0.71   0.481    -.0151634    .0321731
     szint_zs_abs_wb_18t24 |    .003422   .0098716     0.35   0.729    -.0159641     .022808
     szint_zs_abs_wb_25t34 |    .016414   .0124614     1.32   0.188    -.0080579    .0408859
     szint_zs_abs_wb_35t44 |   .0087145   .0094424     0.92   0.356    -.0098285    .0272576
     szint_zs_abs_wb_45t59 |   .0129572    .010426     1.24   0.214    -.0075175    .0334319
    szint_zs_abs_wb_60plus |  -.0035071   .0129482    -0.27   0.787    -.0289351    .0219208
        szint_zs_abs_wb_dt |   .0109118   .0092294     1.18   0.238     -.007213    .0290366
      szint_zs_abs_wb_ausl |   .0085419   .0103218     0.83   0.408    -.0117282    .0288121
          szint_zs_withmig |  -.0040888   .0067955    -0.60   0.548     -.017434    .0092563
           szint_zs_outmig |   .0132538   .0102458     1.29   0.196    -.0068671    .0333747
            szint_zs_inmig |   .0024507   .0109527     0.22   0.823    -.0190584    .0239597
          szint_zs_hh_kids |   .0196337   .0222663     0.88   0.378    -.0240931    .0633606
szint_zs_mpreis_flats_rent |   .0046103    .012378     0.37   0.710    -.0196977    .0289184
          szint_zs_avg_dur |  -.0038087   .0112736    -0.34   0.736     -.025948    .0183306
                     _cons |   .1410786   .0002324   606.97   0.000     .1406222    .1415351
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.94
Statistics robust to heteroskedasticity           Prob > F        =     0.5385
                                                  R-squared       =     0.2937
                                                  Adj R-squared   =     0.1911
                                                  Within R-sq.    =     0.0004
Number of clusters (sb_new)  =        618         Root MSE        =     0.1736

                                             (Std. err. adjusted for 618 clusters in sb_new)
--------------------------------------------------------------------------------------------
                           |               Robust
              treat_consol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       szint_zs_abs_ew_ges |   .0122725   .0111805     1.10   0.273     -.009684    .0342289
     szint_zs_abs_ew_ledig |   .0190104   .0126177     1.51   0.132    -.0057683    .0437892
   szint_zs_abs_ew_married |   .0006784   .0121541     0.06   0.956      -.02319    .0245469
     szint_zs_abs_ew_biodt |  -.0050471    .006649    -0.76   0.448    -.0181045    .0080103
    szint_zs_abs_ew_dtmihi |   .0089701   .0148667     0.60   0.546    -.0202254    .0381657
      szint_zs_abs_ew_ausl |   .0258855   .0157271     1.65   0.100    -.0049997    .0567706
    szint_zs_abs_wb_anteil |  -.0081955   .0084365    -0.97   0.332    -.0247634    .0083723
     szint_zs_abs_wb_18t24 |   .0006773   .0063753     0.11   0.915    -.0118427    .0131972
     szint_zs_abs_wb_25t34 |  -.0066731   .0074027    -0.90   0.368    -.0212106    .0078644
     szint_zs_abs_wb_35t44 |  -.0018031   .0058436    -0.31   0.758    -.0132789    .0096728
     szint_zs_abs_wb_45t59 |  -.0018929   .0074181    -0.26   0.799    -.0164606    .0126748
    szint_zs_abs_wb_60plus |   .0006316   .0096569     0.07   0.948    -.0183327     .019596
        szint_zs_abs_wb_dt |  -.0029101    .005482    -0.53   0.596    -.0136757    .0078555
      szint_zs_abs_wb_ausl |  -.0019064   .0072282    -0.26   0.792    -.0161013    .0122884
          szint_zs_withmig |  -.0015129   .0033134    -0.46   0.648    -.0080198     .004994
           szint_zs_outmig |   .0154324   .0111327     1.39   0.166    -.0064303     .037295
            szint_zs_inmig |   .0111654   .0083463     1.34   0.181    -.0052253    .0275561
          szint_zs_hh_kids |   .0161624   .0133823     1.21   0.228     -.010118    .0424427
szint_zs_mpreis_flats_rent |  -.0061828   .0076476    -0.81   0.419    -.0212013    .0088357
          szint_zs_avg_dur |   .0007006   .0069907     0.10   0.920    -.0130278     .014429
                     _cons |   .0562217   .0001476   380.78   0.000     .0559318    .0565117
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.55
Statistics robust to heteroskedasticity           Prob > F        =     0.9460
                                                  R-squared       =     0.2770
                                                  Adj R-squared   =     0.1720
                                                  Within R-sq.    =     0.0004
Number of clusters (sb_new)  =        618         Root MSE        =     0.2417

                                             (Std. err. adjusted for 618 clusters in sb_new)
--------------------------------------------------------------------------------------------
                           |               Robust
           treat_no_consol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       szint_zs_abs_ew_ges |   .0103401   .0126015     0.82   0.412    -.0144069    .0350872
     szint_zs_abs_ew_ledig |    .011635     .01533     0.76   0.448    -.0184702    .0417402
   szint_zs_abs_ew_married |   .0141909   .0160486     0.88   0.377    -.0173257    .0457075
     szint_zs_abs_ew_biodt |    .011696   .0107823     1.08   0.278    -.0094785    .0328704
    szint_zs_abs_ew_dtmihi |   .0194921   .0181923     1.07   0.284    -.0162343    .0552184
      szint_zs_abs_ew_ausl |   .0019197   .0144152     0.13   0.894     -.026389    .0302284
    szint_zs_abs_wb_anteil |   .0167004   .0114547     1.46   0.145    -.0057946    .0391954
     szint_zs_abs_wb_18t24 |   .0027447   .0085958     0.32   0.750    -.0141358    .0196253
     szint_zs_abs_wb_25t34 |   .0230871   .0119809     1.93   0.054    -.0004412    .0466154
     szint_zs_abs_wb_35t44 |   .0105176   .0088278     1.19   0.234    -.0068186    .0278538
     szint_zs_abs_wb_45t59 |   .0148501   .0092037     1.61   0.107    -.0032243    .0329246
    szint_zs_abs_wb_60plus |  -.0041388   .0105721    -0.39   0.696    -.0249005    .0166229
        szint_zs_abs_wb_dt |   .0138219   .0086783     1.59   0.112    -.0032206    .0308644
      szint_zs_abs_wb_ausl |   .0104484   .0101758     1.03   0.305     -.009535    .0304317
          szint_zs_withmig |  -.0025759   .0063145    -0.41   0.683    -.0149764    .0098245
           szint_zs_outmig |  -.0021786   .0092703    -0.24   0.814    -.0203838    .0160267
            szint_zs_inmig |  -.0087147   .0095873    -0.91   0.364    -.0275424    .0101129
          szint_zs_hh_kids |   .0034714   .0204663     0.17   0.865    -.0367208    .0436635
szint_zs_mpreis_flats_rent |   .0107931   .0112889     0.96   0.339    -.0113762    .0329625
          szint_zs_avg_dur |  -.0045093   .0101269    -0.45   0.656    -.0243966     .015378
                     _cons |   .0848569   .0002319   365.86   0.000     .0844014    .0853124
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       1.21
Statistics robust to heteroskedasticity           Prob > F        =     0.2378
                                                  R-squared       =     0.8543
                                                  Adj R-squared   =     0.8331
                                                  Within R-sq.    =     0.0006
Number of clusters (sb_new)  =        618         Root MSE        =     0.1876

                                             (Std. err. adjusted for 618 clusters in sb_new)
--------------------------------------------------------------------------------------------
                           |               Robust
            ln_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
       szint_zs_abs_ew_ges |  -.0032495   .0122792    -0.26   0.791    -.0273637    .0208646
     szint_zs_abs_ew_ledig |   .0082299   .0150976     0.55   0.586     -.021419    .0378788
   szint_zs_abs_ew_married |  -.0143043   .0156987    -0.91   0.363    -.0451336     .016525
     szint_zs_abs_ew_biodt |  -.0016497   .0122287    -0.13   0.893    -.0256647    .0223652
    szint_zs_abs_ew_dtmihi |  -.0332345   .0169599    -1.96   0.050    -.0665406    .0000715
      szint_zs_abs_ew_ausl |   .0063955   .0150387     0.43   0.671    -.0231377    .0359287
    szint_zs_abs_wb_anteil |  -.0065571   .0115027    -0.57   0.569    -.0291462    .0160321
     szint_zs_abs_wb_18t24 |   .0115457   .0080935     1.43   0.154    -.0043485    .0274399
     szint_zs_abs_wb_25t34 |   .0155072   .0128161     1.21   0.227    -.0096612    .0406756
     szint_zs_abs_wb_35t44 |  -.0035121   .0082721    -0.42   0.671     -.019757    .0127327
     szint_zs_abs_wb_45t59 |  -.0095491   .0087633    -1.09   0.276    -.0267586    .0076604
    szint_zs_abs_wb_60plus |  -.0231404   .0115287    -2.01   0.045    -.0457806   -.0005002
        szint_zs_abs_wb_dt |  -.0096646   .0097191    -0.99   0.320    -.0287511    .0094219
      szint_zs_abs_wb_ausl |     .00638   .0079307     0.80   0.421    -.0091945    .0219545
          szint_zs_withmig |   .0044091   .0041975     1.05   0.294    -.0038341    .0126522
           szint_zs_outmig |  -.0058676    .009081    -0.65   0.518    -.0237011    .0119658
            szint_zs_inmig |   .0108017   .0058444     1.85   0.065    -.0006755     .022279
          szint_zs_hh_kids |   .0233818   .0195553     1.20   0.232    -.0150212    .0617847
szint_zs_mpreis_flats_rent |   .0054593   .0106769     0.51   0.609    -.0155081    .0264267
          szint_zs_avg_dur |   -.011294   .0118758    -0.95   0.342    -.0346159    .0120278
                     _cons |  -.4451609   .0002229 -1997.34   0.000    -.4455986   -.4447232
--------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 
.         ** iib) Export Table for all outcomes
.         * TABLE E3. Reassignment Timing and Changes in Precinct Characteristics
.                 outreg using "$tables/Table_E3_uncond_balance_test_zscores", replay  tex replace
>  fragment
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_E3
> _uncond_balance_test_zscores.tex not found)
                                           {hline 303}
                                              \makecell{Indicator \ (Reassigned =100)}   \makecell
> {Indicator \ (Reassigned >0)}   \makecell{Share \ Reassigned}   \makecell{Share Reassigned \ (Pr
> ecinct Reconfig.)}   \makecell{Share Reassigned \ (Recruitment)}   \makecell{Log \ Avg. Walking 
> Distance}  
                                           {hline 303}
# Residents                                                     0.015                             
>        -0.002                               0.023                                      0.012    
>                                         0.010                                      -0.003       
>            
                                                               (0.014)                            
>        (0.018)                             (0.014)                                    (0.011)   
>                                        (0.013)                                     (0.012)      
>            
# Single Residents                                              0.017                             
>         0.009                               0.031                                      0.019    
>                                         0.012                                       0.008       
>            
                                                               (0.017)                            
>        (0.020)                             (0.016)                                    (0.013)   
>                                        (0.015)                                     (0.015)      
>            
# Married Residents                                             0.007                             
>        -0.019                               0.015                                      0.001    
>                                         0.014                                      -0.014       
>            
                                                               (0.016)                            
>        (0.024)                             (0.018)                                    (0.012)   
>                                        (0.016)                                     (0.016)      
>            
# Native German Residents                                       0.007                             
>        -0.017                               0.007                                     -0.005    
>                                         0.012                                      -0.002       
>            
                                                               (0.010)                            
>        (0.015)                             (0.012)                                    (0.007)   
>                                        (0.011)                                     (0.012)      
>            
# Non-native German Residents                                   0.021                             
>        -0.010                               0.028                                      0.009    
>                                         0.019                                      -0.033       
>            
                                                               (0.020)                            
>        (0.028)                             (0.021)                                    (0.015)   
>                                        (0.018)                                     (0.017)      
>            
# Foreign Residents                                             0.016                             
>         0.016                               0.028                                      0.026    
>                                         0.002                                       0.006       
>            
                                                               (0.019)                            
>        (0.020)                             (0.018)                                    (0.016)   
>                                        (0.014)                                     (0.015)      
>            
# Eligible Voters                                               0.009                             
>        -0.006                               0.009                                     -0.008    
>                                         0.017                                      -0.007       
>            
                                                               (0.013)                            
>        (0.016)                             (0.012)                                    (0.008)   
>                                        (0.011)                                     (0.012)      
>            
# Eligible Voters Aged 18-24                                    0.010                             
>         0.001                               0.003                                      0.001    
>                                         0.003                                       0.012       
>            
                                                               (0.010)                            
>        (0.012)                             (0.010)                                    (0.006)   
>                                        (0.009)                                     (0.008)      
>            
# Eligible Voters Aged 25-34                                    0.005                             
>         0.012                               0.016                                     -0.007    
>                                         0.023                                       0.016       
>            
                                                               (0.012)                            
>        (0.015)                             (0.012)                                    (0.007)   
>                                        (0.012)                                     (0.013)      
>            
# Eligible Voters Aged 35-44                                   -0.003                             
>        -0.006                               0.009                                     -0.002    
>                                         0.011                                      -0.004       
>            
                                                               (0.009)                            
>        (0.012)                             (0.009)                                    (0.006)   
>                                        (0.009)                                     (0.008)      
>            
# Eligible Voters Aged 45-59                                    0.015                             
>        -0.014                               0.013                                     -0.002    
>                                         0.015                                      -0.010       
>            
                                                               (0.010)                            
>        (0.013)                             (0.010)                                    (0.007)   
>                                        (0.009)                                     (0.009)      
>            
# Eligible Voters Aged 60+                                      0.009                             
>        -0.012                               -0.004                                     0.001    
>                                         -0.004                                     -0.023*      
>            
                                                               (0.013)                            
>        (0.015)                             (0.013)                                    (0.010)   
>                                        (0.011)                                     (0.012)      
>            
# German Eligible Voters                                        0.012                             
>        -0.006                               0.011                                     -0.003    
>                                         0.014                                      -0.010       
>            
                                                               (0.009)                            
>        (0.012)                             (0.009)                                    (0.005)   
>                                        (0.009)                                     (0.010)      
>            
# EU Foreigners in the Electorate                               0.002                             
>        -0.004                               0.009                                     -0.002    
>                                         0.010                                       0.006       
>            
                                                               (0.010)                            
>        (0.015)                             (0.010)                                    (0.007)   
>                                        (0.010)                                     (0.008)      
>            
# Within Migration                                             -0.003                             
>         0.001                               -0.004                                    -0.002    
>                                         -0.003                                      0.004       
>            
                                                               (0.006)                            
>        (0.008)                             (0.007)                                    (0.003)   
>                                        (0.006)                                     (0.004)      
>            
# Outmigration                                                  0.011                             
>         0.012                               0.013                                      0.015    
>                                         -0.002                                     -0.006       
>            
                                                               (0.012)                            
>        (0.012)                             (0.010)                                    (0.011)   
>                                        (0.009)                                     (0.009)      
>            
# Inmigration                                                   0.004                             
>         0.004                               0.002                                      0.011    
>                                         -0.009                                      0.011       
>            
                                                               (0.010)                            
>        (0.016)                             (0.011)                                    (0.008)   
>                                        (0.010)                                     (0.006)      
>            
% Households with Children                                     -0.003                             
>        -0.002                               0.020                                      0.016    
>                                         0.003                                       0.023       
>            
                                                               (0.021)                            
>        (0.025)                             (0.022)                                    (0.013)   
>                                        (0.020)                                     (0.020)      
>            
Avg. Quoted Rent per sqm                                        0.015                             
>        -0.005                               0.005                                     -0.006    
>                                         0.011                                       0.005       
>            
                                                               (0.012)                            
>        (0.015)                             (0.012)                                    (0.008)   
>                                        (0.011)                                     (0.011)      
>            
Avg. Duration of Residence                                      0.001                             
>        -0.006                               -0.004                                     0.001    
>                                         -0.005                                     -0.011       
>            
                                                               (0.011)                            
>        (0.014)                             (0.011)                                    (0.007)   
>                                        (0.010)                                     (0.012)      
>            
Observations                                                    4944                              
>         4944                                4944                                       4944     
>                                         4944                                        4944        
>            
$ F$-test on joint insignificance [$ Pr>F$]                  0.54 [  0.95]                        
>      0.70 [  0.83]                       0.59 [  0.92]                              0.94 [  0.54
> ]                                    0.55 [  0.95]                               1.21 [  0.24]  
>            
Precinct FE                                                      X                                
>          X                                    X                                         X       
>                                           X                                          X          
>            
Election FE                                                       X                               
>           X                                   X                                          X      
>                                           X                                           X         
>            
                                           {hline 303}

.                 cleantex         "$tables/Table_E3_uncond_balance_test_zscores.tex", nodis repla
> ce
.                                 
.         ** iiia) Run same regression w/ NON-STDZ covariates
.                 outreg, clear
.                 foreach depvar of varlist fulltreat_simple parttreat_simple treat_simple  treat_
> consol treat_no_consol ln_street_dist {
  2.                          reghdfe  `depvar' int_* $wgt, absorb(i.dup#i.wahl_id i.dup#i.sb_new)
>   vce(cluster sb_new)
  3.                         
.                         
.                         // run F-test: b1 = b2 = ... = 0
.                         local test ""
  4.                         foreach v of varlist int_*{     
  5.                                 local test "`test' _b[`v'] = "
  6.                         }
  7.                         qui test "`test'" 0
  8.                         
.                         // create table
.                         local obs = `e(N)' / `num' 
  9.                         qui outreg, $opt3 noautosumm addrow(Observations, `obs' \ "\$ F\$-tes
> t on joint insignificance [$ Pr>F$]", "`:di %6.2f `r(F)'' [`:di %6.2f `r(p)'']" \ ///
>                                                         Precinct FE, X \ Election FE, X) 
 10.                 }
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.54
Statistics robust to heteroskedasticity           Prob > F        =     0.9513
                                                  R-squared       =     0.2691
                                                  Adj R-squared   =     0.1630
                                                  Within R-sq.    =     0.0003
Number of clusters (sb_new)  =        618         Root MSE        =     0.2606

                                        (Std. err. adjusted for 618 clusters in sb_new)
---------------------------------------------------------------------------------------
                      |               Robust
     fulltreat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       int_abs_ew_ges |   .0379025   .0341493     1.11   0.267    -.0291604    .1049654
     int_abs_ew_ledig |   .0626547    .060237     1.04   0.299    -.0556397    .1809491
   int_abs_ew_married |   .0353696   .0765608     0.46   0.644    -.1149817    .1857209
     int_abs_ew_biodt |   .0431627   .0663942     0.65   0.516    -.0872232    .1735487
    int_abs_ew_dtmihi |   .1233324    .119886     1.03   0.304    -.1121017    .3587664
      int_abs_ew_ausl |    .047908    .057387     0.83   0.404    -.0647896    .1606055
    int_abs_wb_anteil |   .0420044   .0592357     0.71   0.479    -.0743236    .1583325
     int_abs_wb_18t24 |   .1983342   .1979553     1.00   0.317    -.1904136     .587082
     int_abs_wb_25t34 |   .0461644   .1112687     0.41   0.678    -.1723469    .2646758
     int_abs_wb_35t44 |  -.0489474   .1300659    -0.38   0.707    -.3043728    .2064781
     int_abs_wb_45t59 |   .2081943   .1446988     1.44   0.151    -.0759676    .4923561
    int_abs_wb_60plus |   .0634584   .0964544     0.66   0.511    -.1259604    .2528772
        int_abs_wb_dt |   .0869938   .0619006     1.41   0.160    -.0345676    .2085552
      int_abs_wb_ausl |   .0122556   .0665306     0.18   0.854    -.1183982    .1429095
          int_withmig |  -.0000851   .0001625    -0.52   0.601    -.0004042     .000234
           int_outmig |   .0000356   .0000387     0.92   0.359    -.0000404    .0001115
            int_inmig |   .0000124   .0000339     0.37   0.714    -.0000542     .000079
          int_hh_kids |  -.0004485   .0035223    -0.13   0.899    -.0073656    .0064686
int_mpreis_flats_rent |   .0032475   .0026201     1.24   0.216     -.001898    .0083929
          int_avg_dur |   .0001474    .002405     0.06   0.951    -.0045756    .0048704
                _cons |   .0535395   .0270717     1.98   0.048     .0003757    .1067033
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.70
Statistics robust to heteroskedasticity           Prob > F        =     0.8316
                                                  R-squared       =     0.5039
                                                  Adj R-squared   =     0.4318
                                                  Within R-sq.    =     0.0001
Number of clusters (sb_new)  =        618         Root MSE        =     0.3141

                                        (Std. err. adjusted for 618 clusters in sb_new)
---------------------------------------------------------------------------------------
                      |               Robust
     parttreat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       int_abs_ew_ges |   -.005737   .0441392    -0.13   0.897    -.0924183    .0809444
     int_abs_ew_ledig |   .0332077   .0741856     0.45   0.655    -.1124792    .1788946
   int_abs_ew_married |  -.0923242   .1121319    -0.82   0.411    -.3125307    .1278824
     int_abs_ew_biodt |  -.1117702   .0963217    -1.16   0.246    -.3009282    .0773879
    int_abs_ew_dtmihi |  -.0591609   .1675212    -0.35   0.724    -.3881418      .26982
      int_abs_ew_ausl |   .0457783   .0593086     0.77   0.440    -.0706928    .1622494
    int_abs_wb_anteil |  -.0288903   .0736173    -0.39   0.695    -.1734612    .1156807
     int_abs_wb_18t24 |   .0114268   .2475763     0.05   0.963    -.4747675    .4976211
     int_abs_wb_25t34 |   .1084518   .1359011     0.80   0.425     -.158433    .3753366
     int_abs_wb_35t44 |  -.0941065   .1709771    -0.55   0.582    -.4298741    .2416612
     int_abs_wb_45t59 |  -.1957923    .175975    -1.11   0.266     -.541375    .1497903
    int_abs_wb_60plus |  -.0887748   .1108137    -0.80   0.423    -.3063926    .1288429
        int_abs_wb_dt |  -.0448569   .0834387    -0.54   0.591    -.2087152    .1190014
      int_abs_wb_ausl |  -.0242365   .0922906    -0.26   0.793    -.2054783    .1570054
          int_withmig |    .000016   .0002244     0.07   0.943    -.0004248    .0004567
           int_outmig |   .0000382   .0000375     1.02   0.309    -.0000354    .0001118
            int_inmig |   .0000145   .0000554     0.26   0.794    -.0000944    .0001233
          int_hh_kids |  -.0002519   .0040949    -0.06   0.951    -.0082935    .0077896
int_mpreis_flats_rent |  -.0011066   .0032407    -0.34   0.733    -.0074708    .0052576
          int_avg_dur |  -.0012986   .0030884    -0.42   0.674    -.0073637    .0047665
                _cons |    .245791   .0381476     6.44   0.000      .170876    .3207059
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.59
Statistics robust to heteroskedasticity           Prob > F        =     0.9227
                                                  R-squared       =     0.3975
                                                  Adj R-squared   =     0.3100
                                                  Within R-sq.    =     0.0004
Number of clusters (sb_new)  =        618         Root MSE        =     0.2645

                                        (Std. err. adjusted for 618 clusters in sb_new)
---------------------------------------------------------------------------------------
                      |               Robust
         treat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       int_abs_ew_ges |   .0560595   .0345205     1.62   0.105    -.0117323    .1238513
     int_abs_ew_ledig |   .1116517   .0597948     1.87   0.062    -.0057743    .2290776
   int_abs_ew_married |   .0705464   .0847233     0.83   0.405    -.0958346    .2369274
     int_abs_ew_biodt |   .0437322   .0768821     0.57   0.570    -.1072501    .1947145
    int_abs_ew_dtmihi |   .1690668   .1253084     1.35   0.178    -.0770158    .4151494
      int_abs_ew_ausl |   .0820385   .0532059     1.54   0.124     -.022448    .1865251
    int_abs_wb_anteil |   .0399339   .0565902     0.71   0.481    -.0711988    .1510666
     int_abs_wb_18t24 |   .0704397   .2032038     0.35   0.729    -.3286152    .4694947
     int_abs_wb_25t34 |   .1476351   .1120839     1.32   0.188     -.072477    .3677472
     int_abs_wb_35t44 |   .1276799   .1383434     0.92   0.356    -.1440011    .3993609
     int_abs_wb_45t59 |    .178894   .1439468     1.24   0.214     -.103791     .461579
    int_abs_wb_60plus |  -.0257768   .0951672    -0.27   0.787    -.2126676     .161114
        int_abs_wb_dt |   .0776366   .0656664     1.18   0.238    -.0513201    .2065932
      int_abs_wb_ausl |   .0541908   .0654824     0.83   0.408    -.0744047    .1827862
          int_withmig |  -.0001097   .0001823    -0.60   0.548    -.0004678    .0002484
           int_outmig |    .000042   .0000325     1.29   0.196    -.0000218    .0001059
            int_inmig |   8.30e-06   .0000371     0.22   0.823    -.0000646    .0000812
          int_hh_kids |   .0032296   .0036626     0.88   0.378    -.0039631    .0104223
int_mpreis_flats_rent |   .0010144   .0027236     0.37   0.710    -.0043342     .006363
          int_avg_dur |  -.0008567   .0025357    -0.34   0.736    -.0058364     .004123
                _cons |   .0965462   .0290308     3.33   0.001      .039535    .1535574
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.94
Statistics robust to heteroskedasticity           Prob > F        =     0.5385
                                                  R-squared       =     0.2937
                                                  Adj R-squared   =     0.1911
                                                  Within R-sq.    =     0.0004
Number of clusters (sb_new)  =        618         Root MSE        =     0.1736

                                        (Std. err. adjusted for 618 clusters in sb_new)
---------------------------------------------------------------------------------------
                      |               Robust
         treat_consol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       int_abs_ew_ges |    .030425   .0277179     1.10   0.273    -.0240079    .0848578
     int_abs_ew_ledig |   .0692615   .0459704     1.51   0.132     -.021016    .1595389
   int_abs_ew_married |   .0032188   .0576644     0.06   0.956    -.1100235    .1164611
     int_abs_ew_biodt |  -.0331965    .043733    -0.76   0.448    -.1190801     .052687
    int_abs_ew_dtmihi |    .053283   .0883091     0.60   0.546    -.1201398    .2267059
      int_abs_ew_ausl |   .0763745   .0464025     1.65   0.100    -.0147514    .1675004
    int_abs_wb_anteil |  -.0384816   .0396132    -0.97   0.332    -.1162745    .0393114
     int_abs_wb_18t24 |    .013941   .1312335     0.11   0.915    -.2437774    .2716594
     int_abs_wb_25t34 |  -.0600214   .0665833    -0.90   0.368    -.1907788     .070736
     int_abs_wb_35t44 |  -.0264172   .0856174    -0.31   0.758     -.194554    .1417196
     int_abs_wb_45t59 |  -.0261349   .1024176    -0.26   0.799    -.2272642    .1749944
    int_abs_wb_60plus |   .0046424   .0709765     0.07   0.948    -.1347423    .1440272
        int_abs_wb_dt |  -.0207054   .0390039    -0.53   0.596    -.0973019    .0558911
      int_abs_wb_ausl |  -.0120946   .0458563    -0.26   0.792     -.102148    .0779587
          int_withmig |  -.0000406   .0000889    -0.46   0.648    -.0002152     .000134
           int_outmig |   .0000489   .0000353     1.39   0.166    -.0000204    .0001183
            int_inmig |   .0000378   .0000283     1.34   0.181    -.0000177    .0000934
          int_hh_kids |   .0026586   .0022013     1.21   0.228    -.0016643    .0069815
int_mpreis_flats_rent |  -.0013604   .0016827    -0.81   0.419     -.004665    .0019442
          int_avg_dur |   .0001576   .0015724     0.10   0.920    -.0029303    .0032455
                _cons |   .0496694   .0191657     2.59   0.010     .0120315    .0873073
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       0.55
Statistics robust to heteroskedasticity           Prob > F        =     0.9460
                                                  R-squared       =     0.2770
                                                  Adj R-squared   =     0.1720
                                                  Within R-sq.    =     0.0004
Number of clusters (sb_new)  =        618         Root MSE        =     0.2417

                                        (Std. err. adjusted for 618 clusters in sb_new)
---------------------------------------------------------------------------------------
                      |               Robust
      treat_no_consol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       int_abs_ew_ges |   .0256345   .0312407     0.82   0.412    -.0357165    .0869856
     int_abs_ew_ledig |   .0423902   .0558522     0.76   0.448    -.0672933    .1520737
   int_abs_ew_married |   .0673276   .0761417     0.88   0.377    -.0822006    .2168559
     int_abs_ew_biodt |   .0769287   .0709193     1.08   0.278    -.0623438    .2162012
    int_abs_ew_dtmihi |   .1157838   .1080631     1.07   0.284    -.0964323    .3279999
      int_abs_ew_ausl |   .0056641   .0425316     0.13   0.894    -.0778602    .0891883
    int_abs_wb_anteil |   .0784155   .0537848     1.46   0.145    -.0272081     .184039
     int_abs_wb_18t24 |   .0564987   .1769412     0.32   0.750    -.2909812    .4039787
     int_abs_wb_25t34 |   .2076565    .107762     1.93   0.054    -.0039682    .4192813
     int_abs_wb_35t44 |   .1540971   .1293393     1.19   0.234    -.0999014    .4080957
     int_abs_wb_45t59 |   .2050289   .1270717     1.61   0.107    -.0445167    .4545744
    int_abs_wb_60plus |  -.0304192   .0777031    -0.39   0.696    -.1830139    .1221755
        int_abs_wb_dt |   .0983419   .0617452     1.59   0.112    -.0229144    .2195982
      int_abs_wb_ausl |   .0662854   .0645561     1.03   0.305    -.0604909    .1930617
          int_withmig |  -.0000691   .0001694    -0.41   0.683    -.0004018    .0002636
           int_outmig |  -6.91e-06   .0000294    -0.24   0.814    -.0000647    .0000508
            int_inmig |  -.0000295   .0000325    -0.91   0.364    -.0000933    .0000343
          int_hh_kids |    .000571   .0033666     0.17   0.865    -.0060403    .0071823
int_mpreis_flats_rent |   .0023749   .0024839     0.96   0.339    -.0025032    .0072529
          int_avg_dur |  -.0010143   .0022778    -0.45   0.656    -.0054874    .0034589
                _cons |   .0468768   .0277826     1.69   0.092    -.0076832    .1014368
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =     98,880
Absorbing 2 HDFE groups                           F(  20,    617) =       1.21
Statistics robust to heteroskedasticity           Prob > F        =     0.2378
                                                  R-squared       =     0.8543
                                                  Adj R-squared   =     0.8331
                                                  Within R-sq.    =     0.0006
Number of clusters (sb_new)  =        618         Root MSE        =     0.1876

                                        (Std. err. adjusted for 618 clusters in sb_new)
---------------------------------------------------------------------------------------
                      |               Robust
       ln_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
       int_abs_ew_ges |   -.008056   .0304418    -0.26   0.791    -.0678381    .0517261
     int_abs_ew_ledig |   .0299842   .0550057     0.55   0.586    -.0780369    .1380053
   int_abs_ew_married |  -.0678657   .0744812    -0.91   0.363    -.2141332    .0784017
     int_abs_ew_biodt |  -.0108509   .0804329    -0.13   0.893    -.1688064    .1471045
    int_abs_ew_dtmihi |  -.1974148   .1007425    -1.96   0.050    -.3952545    .0004249
      int_abs_ew_ausl |   .0188698   .0443712     0.43   0.671    -.0682671    .1060067
    int_abs_wb_anteil |  -.0307883     .05401    -0.57   0.569     -.136854    .0752774
     int_abs_wb_18t24 |   .2376643   .1666024     1.43   0.154    -.0895123    .5648408
     int_abs_wb_25t34 |   .1394791   .1152738     1.21   0.227    -.0868974    .3658556
     int_abs_wb_35t44 |  -.0514574   .1211971    -0.42   0.671    -.2894663    .1865514
     int_abs_wb_45t59 |  -.1318399   .1209909    -1.09   0.276    -.3694438    .1057639
    int_abs_wb_60plus |  -.1700777   .0847338    -2.01   0.045    -.3364794   -.0036761
        int_abs_wb_dt |  -.0687628   .0691506    -0.99   0.320    -.2045619    .0670363
      int_abs_wb_ausl |   .0404752   .0503133     0.80   0.421    -.0583309    .1392814
          int_withmig |   .0001183   .0001126     1.05   0.294    -.0001029    .0003395
           int_outmig |  -.0000186   .0000288    -0.65   0.518    -.0000752     .000038
            int_inmig |   .0000366   .0000198     1.85   0.065    -2.29e-06    .0000755
          int_hh_kids |   .0038461   .0032167     1.20   0.232    -.0024709    .0101631
int_mpreis_flats_rent |   .0012012   .0023493     0.51   0.609    -.0034123    .0058148
          int_avg_dur |  -.0025403   .0026712    -0.95   0.342     -.007786    .0027054
                _cons |  -.4320877   .0289411   -14.93   0.000    -.4889226   -.3752528
---------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-------------------------------------------------------+
   Absorbed FE | Categories  - Redundant  = Num. Coefs |
---------------+---------------------------------------|
   dup#wahl_id |       160           1         159     |
    dup#sb_new |     12360       12360           0    *|
-------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 
.         ** iiib) Export Table for all outcomes
.         * TABLE E4. Reassignment Timing and Changes in Precinct Characteristics (Non-standardize
> d)
.                 outreg using "$tables/Table_E4_uncond_balance_test_nosz", replay tex replace fra
> gment
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_E4
> _uncond_balance_test_nosz.tex not found)
                                           {hline 303}
                                              \makecell{Indicator \ (Reassigned =100)}   \makecell
> {Indicator \ (Reassigned >0)}   \makecell{Share \ Reassigned}   \makecell{Share Reassigned \ (Pr
> ecinct Reconfig.)}   \makecell{Share Reassigned \ (Recruitment)}   \makecell{Log \ Avg. Walking 
> Distance}  
                                           {hline 303}
# Residents                                                     0.038                             
>        -0.006                               0.056                                      0.030    
>                                         0.026                                      -0.008       
>            
                                                               (0.034)                            
>        (0.044)                             (0.035)                                    (0.028)   
>                                        (0.031)                                     (0.030)      
>            
# Single Residents                                              0.063                             
>         0.033                               0.112                                      0.069    
>                                         0.042                                       0.030       
>            
                                                               (0.060)                            
>        (0.074)                             (0.060)                                    (0.046)   
>                                        (0.056)                                     (0.055)      
>            
# Married Residents                                             0.035                             
>        -0.092                               0.071                                      0.003    
>                                         0.067                                      -0.068       
>            
                                                               (0.077)                            
>        (0.112)                             (0.085)                                    (0.058)   
>                                        (0.076)                                     (0.074)      
>            
# Native German Residents                                       0.043                             
>        -0.112                               0.044                                     -0.033    
>                                         0.077                                      -0.011       
>            
                                                               (0.066)                            
>        (0.096)                             (0.077)                                    (0.044)   
>                                        (0.071)                                     (0.080)      
>            
# Non-native German Residents                                   0.123                             
>        -0.059                               0.169                                      0.053    
>                                         0.116                                      -0.197       
>            
                                                               (0.120)                            
>        (0.168)                             (0.125)                                    (0.088)   
>                                        (0.108)                                     (0.101)      
>            
# Foreign Residents                                             0.048                             
>         0.046                               0.082                                      0.076    
>                                         0.006                                       0.019       
>            
                                                               (0.057)                            
>        (0.059)                             (0.053)                                    (0.046)   
>                                        (0.043)                                     (0.044)      
>            
# Eligible Voters                                               0.042                             
>        -0.029                               0.040                                     -0.038    
>                                         0.078                                      -0.031       
>            
                                                               (0.059)                            
>        (0.074)                             (0.057)                                    (0.040)   
>                                        (0.054)                                     (0.054)      
>            
# Eligible Voters Aged 18-24                                    0.198                             
>         0.011                               0.070                                      0.014    
>                                         0.056                                       0.238       
>            
                                                               (0.198)                            
>        (0.248)                             (0.203)                                    (0.131)   
>                                        (0.177)                                     (0.167)      
>            
# Eligible Voters Aged 25-34                                    0.046                             
>         0.108                               0.148                                     -0.060    
>                                         0.208                                       0.139       
>            
                                                               (0.111)                            
>        (0.136)                             (0.112)                                    (0.067)   
>                                        (0.108)                                     (0.115)      
>            
# Eligible Voters Aged 35-44                                   -0.049                             
>        -0.094                               0.128                                     -0.026    
>                                         0.154                                      -0.051       
>            
                                                               (0.130)                            
>        (0.171)                             (0.138)                                    (0.086)   
>                                        (0.129)                                     (0.121)      
>            
# Eligible Voters Aged 45-59                                    0.208                             
>        -0.196                               0.179                                     -0.026    
>                                         0.205                                      -0.132       
>            
                                                               (0.145)                            
>        (0.176)                             (0.144)                                    (0.102)   
>                                        (0.127)                                     (0.121)      
>            
# Eligible Voters Aged 60+                                      0.063                             
>        -0.089                               -0.026                                     0.005    
>                                         -0.030                                     -0.170*      
>            
                                                               (0.096)                            
>        (0.111)                             (0.095)                                    (0.071)   
>                                        (0.078)                                     (0.085)      
>            
# German Eligible Voters                                        0.087                             
>        -0.045                               0.078                                     -0.021    
>                                         0.098                                      -0.069       
>            
                                                               (0.062)                            
>        (0.083)                             (0.066)                                    (0.039)   
>                                        (0.062)                                     (0.069)      
>            
# EU Foreigners in the Electorate                               0.012                             
>        -0.024                               0.054                                     -0.012    
>                                         0.066                                       0.040       
>            
                                                               (0.067)                            
>        (0.092)                             (0.065)                                    (0.046)   
>                                        (0.065)                                     (0.050)      
>            
# Within Migration                                             -0.000                             
>         0.000                               -0.000                                    -0.000    
>                                         -0.000                                      0.000       
>            
                                                               (0.000)                            
>        (0.000)                             (0.000)                                    (0.000)   
>                                        (0.000)                                     (0.000)      
>            
# Outmigration                                                  0.000                             
>         0.000                               0.000                                      0.000    
>                                         -0.000                                     -0.000       
>            
                                                               (0.000)                            
>        (0.000)                             (0.000)                                    (0.000)   
>                                        (0.000)                                     (0.000)      
>            
# Inmigration                                                   0.000                             
>         0.000                               0.000                                      0.000    
>                                         -0.000                                      0.000       
>            
                                                               (0.000)                            
>        (0.000)                             (0.000)                                    (0.000)   
>                                        (0.000)                                     (0.000)      
>            
\% Households with Children                                    -0.000                             
>        -0.000                               0.003                                      0.003    
>                                         0.001                                       0.004       
>            
                                                               (0.004)                            
>        (0.004)                             (0.004)                                    (0.002)   
>                                        (0.003)                                     (0.003)      
>            
Avg. Quoted Rent per sqm (euros)                                0.003                             
>        -0.001                               0.001                                     -0.001    
>                                         0.002                                       0.001       
>            
                                                               (0.003)                            
>        (0.003)                             (0.003)                                    (0.002)   
>                                        (0.002)                                     (0.002)      
>            
Avg. Duration of Residence (years)                              0.000                             
>        -0.001                               -0.001                                     0.000    
>                                         -0.001                                     -0.003       
>            
                                                               (0.002)                            
>        (0.003)                             (0.003)                                    (0.002)   
>                                        (0.002)                                     (0.003)      
>            
Observations                                                    4944                              
>         4944                                4944                                       4944     
>                                         4944                                        4944        
>            
$ F$-test on joint insignificance [$ Pr>F$]                  0.54 [  0.95]                        
>      0.70 [  0.83]                       0.59 [  0.92]                              0.94 [  0.54
> ]                                    0.55 [  0.95]                               1.21 [  0.24]  
>            
Precinct FE                                                      X                                
>          X                                    X                                         X       
>                                           X                                          X          
>            
Election FE                                                       X                               
>           X                                   X                                          X      
>                                           X                                           X         
>            
                                           {hline 303}

.                 cleantex         "$tables/Table_E4_uncond_balance_test_nosz.tex", nodis replace
.         
.         
.     ** iv) Visualisation with COEFPLOTS (stdz coefs)
.         
.         * PLOT: FIGURE 6. Reassignment Timing and Changes in Precinct Characteristics
.                   coefplot  (fulltreat_simple_zs, mcol(black) ms(O) msize(vsmall) ciopt(color(bl
> ack) recast(rcap)) levels(95) mlw(.3) legend(off)), ///
>                                 bylabel("{bf:Panel A.}" "Reassigned")  ///
>                         || (ln_street_dist_zs, mcol(black) ms(O) msize(vsmall) ciopt(color(black
> ) recast(rcap)) levels(95) mlw(.3) legend(off)), ///
>                                 bylabel("{bf:Panel B.}" "Log Walking Distance") ///
>                         || ,  drop(_cons) xline(0, lpattern(solid) lcol(black)) aspect(1) coefla
> b(,labsize(medsmall)) ///
>                         xlab(-.05(.05).1,labsize(medsmall) nogrid) xtick(, grid ) ytick(,grid gl
> sty(solid) glcol(black%20))  ///
>                         subtitle(,nobox bexpand justification(left) size(medsmall)) name(g1, rep
> lace)
.                         
.           coefplot  (treat_consol_zs, mcol(black) ms(O) msize(vsmall) ciopt(color(black) recast(
> rcap)) levels(95) mlw(.3) legend(off)) , ///
>                 bylabel("{bf:Panel C.}" "Share Reassigned (Precinct Reconfiguration)")  ///
>                         || (treat_no_consol_zs, mcol(black) ms(O) msize(vsmall) ciopt(color(blac
> k) recast(rcap)) levels(95) mlw(.3) legend(off)), ///
>                                 bylabel("{bf:Panel D.}" "Share Reassigned (Recruitment)")  ///
>                         || ,  drop(_cons) xline(0, lpattern(solid) lcol(black)) aspect(1) coefla
> b(,labsize(medsmall)) ///
>                         xlab(-.05(.05).1,labsize(medsmall) nogrid) xtick(, grid ) ytick(,grid gl
> sty(solid) glcol(black%20))  ///
>                         subtitle(,nobox bexpand justification(left) size(medsmall))  name(g2, re
> place)
.                         
.                         graph combine g1 g2, row(2) imargin(zero) ycommon iscale(.6)
.         
.                         graph export "$figures/Figure_6_COEFPLOT_balancetest_z_FEs.pdf", replace
>                                 
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_6_COEF
    > PLOT_balancetest_z_FEs.pdf saved as PDF format
.           
. }

. 
end of do-file
Running: 03a_main_figure_7.do

. /*
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
>         
> Output: Figure 7
> 
> 
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         ** means of outcomes (weighted by #eligible voters)
.         su turnout_urne turnout_pos_req turnout_tot_req share_mail [aw=wahlber_gesamt]

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
turnout_urne |   4,944     7710778    33.69375   9.199333   9.938712   55.85818
turnout_po~q |   4,944     7710778    28.70511   7.720665   4.014273   51.98697
turnout_to~q |   4,944     7710778    62.39885   14.80721   15.10321   91.71677
  share_mail |   4,944     7710778    46.06192   6.773813   18.12865   69.44569

.                 
.         
. ********************************************************************************
.  //      Baseline: Event Study Illustration TWFE 4-way figure (Figure 7) //
. ********************************************************************************        
.         
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         order F1event, last

.         
.         // Estimate baseline ES
.         estimates clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req share_mail treat_simpl
> e ln_street_dist del_street_dist        {
  2.                  reghdfe `v' F7event-L7event F1event $ctr $wgt if smpl_trim==1, absorb(i.wahl
> _id#i.stadtbez i.sb_new) cluster(sb_new)
  3.                 
.                 estimates store `v'
  4.         }
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      17.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9658
                                                  Within R-sq.    =     0.1691
Number of clusters (sb_new)  =        618         Root MSE        =     1.6979

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3609916    -0.46   0.646    -.8749677    .5428746
          F6event |   .0365542   .3229698     0.11   0.910    -.5976991    .6708075
          F5event |   .2427259   .2553086     0.95   0.342    -.2586533    .7441051
          F4event |   .0132213   .1747424     0.08   0.940    -.3299406    .3563833
          F3event |  -.0565081    .169943    -0.33   0.740    -.3902449    .2772287
          F2event |    .006289   .1214372     0.05   0.959    -.2321913    .2447694
          L0event |  -.9985596   .2347416    -4.25   0.000    -1.459549   -.5375702
          L1event |   -.892731   .2344548    -3.81   0.000    -1.353157   -.4323048
          L2event |  -.7515249   .2578216    -2.91   0.004    -1.257839   -.2452106
          L3event |  -.2931705   .2663112    -1.10   0.271    -.8161567    .2298157
          L4event |  -.8782313   .4731179    -1.86   0.064    -1.807348    .0508853
          L5event |  -.5487131   .6172865    -0.89   0.374     -1.76095    .6635241
          L6event |    1.07717   .7208125     1.49   0.136    -.3383731    2.492714
          L7event |   .9427971   1.187553     0.79   0.428    -1.389339    3.274933
          F1event |          0  (omitted)
        ln_ew_ges |  -.7878053   .9548651    -0.83   0.410    -2.662985    1.087374
         ew_biodt |    .373603   .0281093    13.29   0.000     .3184015    .4288045
        ew_dtmihi |   .0630129   .0513196     1.23   0.220    -.0377693    .1637951
         ew_ledig |   .2127399   .0574696     3.70   0.000     .0998801    .3255996
       ew_married |   .4312672   .0590852     7.30   0.000     .3152348    .5472997
        wb_anteil |  -.2895078   .0204524   -14.16   0.000    -.3296726   -.2493431
          wb_ausl |   .0153767   .0162706     0.95   0.345    -.0165758    .0473293
         wb_18t24 |  -.0164981   .0295896    -0.56   0.577    -.0746067    .0416105
         wb_25t34 |  -.0724461   .0193717    -3.74   0.000    -.1104886   -.0344037
         wb_35t44 |    .006101   .0230068     0.27   0.791    -.0390802    .0512822
         wb_45t59 |   .0140084   .0220328     0.64   0.525      -.02926    .0572768
          avg_dur |  -.0288813   .0210954    -1.37   0.171    -.0703088    .0125462
          hh_kids |  -.0506236   .0408723    -1.24   0.216    -.1308893    .0296421
mpreis_flats_rent |   .0303932   .0255105     1.19   0.234    -.0197047    .0804911
            _cons |   11.36038   9.043266     1.26   0.210    -6.398933    29.11969
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9616
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2040
Number of clusters (sb_new)  =        618         Root MSE        =     1.6847

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3314703     0.25   0.801    -.5673098    .7345839
          F6event |   .2184141   .2600152     0.84   0.401    -.2922081    .7290362
          F5event |  -.4796951   .2637694    -1.82   0.069    -.9976898    .0382995
          F4event |  -.2325114   .1614186    -1.44   0.150    -.5495078     .084485
          F3event |   .0074853   .1524588     0.05   0.961    -.2919157    .3068862
          F2event |   -.059996   .1248273    -0.48   0.631    -.3051339    .1851419
          L0event |   .6092887   .2189284     2.78   0.006     .1793535    1.039224
          L1event |   .9038027   .2273231     3.98   0.000      .457382    1.350223
          L2event |   1.047491   .2631015     3.98   0.000     .5308081    1.564174
          L3event |   .4175165   .2577372     1.62   0.106    -.0886321    .9236651
          L4event |   1.531036   .6502778     2.35   0.019     .2540104    2.808063
          L5event |   2.356691   .5459981     4.32   0.000     1.284451    3.428931
          L6event |  -.3848372    .877942    -0.44   0.661    -2.108954     1.33928
          L7event |   -.503072    .765562    -0.66   0.511    -2.006495    1.000351
          F1event |          0  (omitted)
        ln_ew_ges |   2.465914   1.349016     1.83   0.068    -.1833058    5.115133
         ew_biodt |    .388145   .0296323    13.10   0.000     .3299526    .4463375
        ew_dtmihi |  -.2303872   .0595829    -3.87   0.000     -.347397   -.1133774
         ew_ledig |   .2111155   .0802108     2.63   0.009     .0535962    .3686348
       ew_married |   .2074512   .0799261     2.60   0.010     .0504911    .3644114
        wb_anteil |  -.2425344   .0223631   -10.85   0.000    -.2864513   -.1986174
          wb_ausl |   -.068913   .0145836    -4.73   0.000    -.0975526   -.0402734
         wb_18t24 |  -.0273058   .0275422    -0.99   0.322    -.0813937    .0267821
         wb_25t34 |   .0526598   .0194716     2.70   0.007     .0144212    .0908985
         wb_35t44 |  -.0089296   .0249211    -0.36   0.720    -.0578702    .0400109
         wb_45t59 |   -.038132   .0205362    -1.86   0.064    -.0784614    .0021973
          avg_dur |   .0459808   .0233899     1.97   0.050     .0000472    .0919144
          hh_kids |  -.0642629   .0417916    -1.54   0.125    -.1463339    .0178081
mpreis_flats_rent |  -.0155286   .0234069    -0.66   0.507    -.0614954    .0304383
            _cons |  -11.72708   11.35784    -1.03   0.302     -34.0318    10.57764
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4395
Number of clusters (sb_new)  =        618         Root MSE        =     1.6245

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3259399    -0.25   0.800    -.7224958    .5576764
          F6event |   .2549675   .2761707     0.92   0.356    -.2873811    .7973161
          F5event |  -.2369689   .2619342    -0.90   0.366    -.7513594    .2774217
          F4event |  -.2192898   .1694872    -1.29   0.196    -.5521315     .113552
          F3event |  -.0490231   .1579758    -0.31   0.756    -.3592586    .2612124
          F2event |  -.0537065   .1322374    -0.41   0.685    -.3133964    .2059834
          L0event |  -.3892706   .1633916    -2.38   0.018    -.7101417   -.0683996
          L1event |    .011072    .200952     0.06   0.956    -.3835608    .4057048
          L2event |   .2959665    .224252     1.32   0.187    -.1444233    .7363563
          L3event |   .1243464   .2332215     0.53   0.594    -.3336578    .5823505
          L4event |   .6528057   .6207241     1.05   0.293    -.5661824    1.871794
          L5event |   1.807978   .7132727     2.53   0.011      .407241    3.208714
          L6event |   .6923315   .7952045     0.87   0.384     -.869304    2.253967
          L7event |   .4397263   1.099927     0.40   0.689    -1.720328    2.599781
          F1event |          0  (omitted)
        ln_ew_ges |   1.678109   1.068642     1.57   0.117    -.4205078    3.776725
         ew_biodt |    .761748   .0314252    24.24   0.000     .7000348    .8234612
        ew_dtmihi |  -.1673742   .0522282    -3.20   0.001    -.2699407   -.0648076
         ew_ledig |   .4238555   .0699086     6.06   0.000     .2865679    .5611432
       ew_married |   .6387186   .0688582     9.28   0.000     .5034938    .7739433
        wb_anteil |  -.5320422   .0239517   -22.21   0.000     -.579079   -.4850055
          wb_ausl |  -.0535363   .0175921    -3.04   0.002     -.088084   -.0189885
         wb_18t24 |  -.0438039    .025903    -1.69   0.091    -.0946727    .0070648
         wb_25t34 |  -.0197863   .0166674    -1.19   0.236     -.052518    .0129454
         wb_35t44 |  -.0028286   .0208991    -0.14   0.892    -.0438706    .0382134
         wb_45t59 |  -.0241236   .0196706    -1.23   0.221    -.0627531    .0145059
          avg_dur |   .0170995    .022054     0.78   0.438    -.0262106    .0604096
          hh_kids |  -.1148866    .035755    -3.21   0.001    -.1851028   -.0446704
mpreis_flats_rent |   .0148646   .0236111     0.63   0.529    -.0315033    .0612326
            _cons |   -.366715   10.03193    -0.04   0.971    -20.06759    19.33416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =       3.66
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9138
                                                  Adj R-squared   =     0.8948
                                                  Within R-sq.    =     0.0548
Number of clusters (sb_new)  =        618         Root MSE        =     2.1361

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
       share_mail | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0482375   .4551545    -0.11   0.916    -.9420774    .8456024
          F6event |  -.2319078   .3690895    -0.63   0.530    -.9567318    .4929162
          F5event |  -.5037323     .31414    -1.60   0.109    -1.120645    .1131809
          F4event |  -.2503892   .2294437    -1.09   0.276    -.7009745    .2001961
          F3event |  -.2059927   .2094715    -0.98   0.326    -.6173563    .2053708
          F2event |  -.0672903   .1621961    -0.41   0.678    -.3858137    .2512331
          L0event |   .9440995   .2782183     3.39   0.001     .3977299    1.490469
          L1event |   1.012785   .2990849     3.39   0.001     .4254376    1.600133
          L2event |   1.149001   .3562534     3.23   0.001     .4493847    1.848617
          L3event |   .5406971   .3857466     1.40   0.162    -.2168383    1.298233
          L4event |    1.17675   .6422871     1.83   0.067    -.0845838    2.438084
          L5event |   1.685944   .6684353     2.52   0.012       .37326    2.998628
          L6event |   -1.27539    .902275    -1.41   0.158    -3.047292    .4965122
          L7event |  -1.217961   1.371742    -0.89   0.375     -3.91181    1.475889
          F1event |          0  (omitted)
        ln_ew_ges |   2.257213   1.549976     1.46   0.146    -.7866554    5.301081
         ew_biodt |  -.0045435   .0367622    -0.12   0.902    -.0767376    .0676507
        ew_dtmihi |  -.2664461   .0777644    -3.43   0.001    -.4191611   -.1137311
         ew_ledig |   .0730852   .0944779     0.77   0.439     -.112452    .2586224
       ew_married |  -.0941475   .0942588    -1.00   0.318    -.2792546    .0909595
        wb_anteil |   .0843114   .0276248     3.05   0.002     .0300613    .1385615
          wb_ausl |  -.0357221   .0186546    -1.91   0.056    -.0723564    .0009121
         wb_18t24 |  -.0349527   .0374242    -0.93   0.351    -.1084469    .0385414
         wb_25t34 |   .0712579   .0241091     2.96   0.003      .023912    .1186039
         wb_35t44 |   .0032176   .0292375     0.11   0.912    -.0541995    .0606348
         wb_45t59 |  -.0282287   .0259062    -1.09   0.276    -.0791038    .0226464
          avg_dur |     .02262    .027609     0.82   0.413    -.0315991    .0768391
          hh_kids |  -.0229457    .056149    -0.41   0.683     -.133212    .0873206
mpreis_flats_rent |  -.0178973   .0311389    -0.57   0.566    -.0790483    .0432537
            _cons |   26.47949   13.49504     1.96   0.050    -.0222817    52.98126
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =     216.82
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7981
                                                  Adj R-squared   =     0.7536
                                                  Within R-sq.    =     0.6240
Number of clusters (sb_new)  =        618         Root MSE        =     0.1388

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     treat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0632516   .0237125    -2.67   0.008    -.1098187   -.0166846
          F6event |  -.0632953   .0159697    -3.96   0.000    -.0946568   -.0319337
          F5event |  -.0778128   .0186639    -4.17   0.000    -.1144653   -.0411603
          F4event |  -.0392748   .0132025    -2.97   0.003     -.065202   -.0133476
          F3event |  -.0284923   .0153518    -1.86   0.064    -.0586403    .0016557
          F2event |  -.0345775   .0115693    -2.99   0.003    -.0572975   -.0118576
          L0event |   .8026337   .0143078    56.10   0.000     .7745359    .8307316
          L1event |  -.0215248   .0148113    -1.45   0.147    -.0506115    .0075619
          L2event |  -.0020519   .0175117    -0.12   0.907    -.0364416    .0323378
          L3event |   .0898135   .0352978     2.54   0.011     .0204952    .1591319
          L4event |  -.0471656   .0376111    -1.25   0.210    -.1210269    .0266957
          L5event |  -.0466684   .0474175    -0.98   0.325    -.1397876    .0464509
          L6event |   .0100201   .0241524     0.41   0.678    -.0374107    .0574509
          L7event |  -.1025236   .0699702    -1.47   0.143    -.2399323    .0348851
          F1event |          0  (omitted)
        ln_ew_ges |  -.0432123   .0937594    -0.46   0.645    -.2273384    .1409139
         ew_biodt |  -.0018957   .0024798    -0.76   0.445    -.0067655    .0029741
        ew_dtmihi |   -.006061    .003756    -1.61   0.107     -.013437     .001315
         ew_ledig |   .0063836   .0039139     1.63   0.103    -.0013027    .0140698
       ew_married |   .0022307   .0038555     0.58   0.563    -.0053408    .0098023
        wb_anteil |   .0011873    .001877     0.63   0.527    -.0024988    .0048734
          wb_ausl |  -.0010877   .0008089    -1.34   0.179    -.0026763    .0005009
         wb_18t24 |  -.0031615   .0018231    -1.73   0.083    -.0067419    .0004188
         wb_25t34 |  -.0001637   .0010747    -0.15   0.879    -.0022743    .0019468
         wb_35t44 |   .0005616   .0016016     0.35   0.726    -.0025836    .0037067
         wb_45t59 |    .000313   .0014109     0.22   0.825    -.0024578    .0030837
          avg_dur |   .0009515   .0013559     0.70   0.483    -.0017113    .0036142
          hh_kids |    .003697   .0029263     1.26   0.207    -.0020498    .0094437
mpreis_flats_rent |  -.0019302    .001874    -1.03   0.303    -.0056105    .0017501
            _cons |   .0998949   .8360394     0.12   0.905    -1.541933    1.741723
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =       2.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8819
                                                  Adj R-squared   =     0.8558
                                                  Within R-sq.    =     0.0433
Number of clusters (sb_new)  =        618         Root MSE        =     0.1742

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
   ln_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0266834   .0291603     0.92   0.361     -.030582    .0839488
          F6event |  -.0022512     .02781    -0.08   0.936    -.0568649    .0523625
          F5event |  -.0071962   .0249162    -0.29   0.773     -.056127    .0417346
          F4event |  -.0008457   .0145466    -0.06   0.954    -.0294127    .0277212
          F3event |  -.0115022   .0130031    -0.88   0.377     -.037038    .0140335
          F2event |   .0051287   .0094591     0.54   0.588    -.0134472    .0237047
          L0event |   .1225514    .030146     4.07   0.000     .0633503    .1817525
          L1event |   .0780487   .0371141     2.10   0.036     .0051634     .150934
          L2event |    .093644   .0433888     2.16   0.031     .0084364    .1788515
          L3event |  -.0168031   .0413032    -0.41   0.684     -.097915    .0643089
          L4event |   .0873439   .0559599     1.56   0.119     -.022551    .1972388
          L5event |   .0455746   .0647217     0.70   0.482    -.0815268    .1726761
          L6event |   .0592468   .0600528     0.99   0.324    -.0586859    .1771795
          L7event |  -.0345805   .0737778    -0.47   0.639    -.1794667    .1103056
          F1event |          0  (omitted)
        ln_ew_ges |  -.2062071   .1244284    -1.66   0.098    -.4505616    .0381474
         ew_biodt |  -.0004266   .0030745    -0.14   0.890    -.0064643    .0056111
        ew_dtmihi |  -.0087278   .0061617    -1.42   0.157    -.0208283    .0033727
         ew_ledig |   .0089718   .0085368     1.05   0.294    -.0077928    .0257365
       ew_married |  -.0028219   .0082314    -0.34   0.732    -.0189869    .0133431
        wb_anteil |  -.0001012     .00195    -0.05   0.959    -.0039306    .0037283
          wb_ausl |    .000791    .001277     0.62   0.536    -.0017169    .0032988
         wb_18t24 |    .002156   .0033347     0.65   0.518    -.0043927    .0087047
         wb_25t34 |   .0063444   .0024258     2.62   0.009     .0015805    .0111083
         wb_35t44 |  -.0012813    .002748    -0.47   0.641    -.0066779    .0041152
         wb_45t59 |   .0008407   .0025658     0.33   0.743     -.004198    .0058794
          avg_dur |   .0021056   .0028191     0.75   0.455    -.0034307    .0076419
          hh_kids |   .0070431   .0055117     1.28   0.202    -.0037808     .017867
mpreis_flats_rent |   .0023165   .0030547     0.76   0.449    -.0036824    .0083153
            _cons |   .5997727   1.230865     0.49   0.626    -1.817419    3.016965
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =       2.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.2134
                                                  Adj R-squared   =     0.0396
                                                  Within R-sq.    =     0.0581
Number of clusters (sb_new)  =        618         Root MSE        =     0.1171

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  del_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   -.010359    .016089    -0.64   0.520    -.0419548    .0212367
          F6event |  -.0159365   .0144842    -1.10   0.272    -.0443808    .0125077
          F5event |  -.0038887   .0183259    -0.21   0.832    -.0398775    .0321001
          F4event |  -.0016578   .0077734    -0.21   0.831    -.0169234    .0136078
          F3event |  -.0061827   .0088582    -0.70   0.485    -.0235785    .0112131
          F2event |   .0081598   .0075661     1.08   0.281    -.0066986    .0230183
          L0event |   .1168431   .0241259     4.84   0.000     .0694642    .1642219
          L1event |   .0019224   .0100908     0.19   0.849     -.017894    .0217388
          L2event |   .0091624   .0149653     0.61   0.541    -.0202267    .0385515
          L3event |  -.0501039   .0254478    -1.97   0.049    -.1000787    -.000129
          L4event |   .0267624   .0276078     0.97   0.333    -.0274541     .080979
          L5event |  -.0425727   .0321038    -1.33   0.185    -.1056187    .0204732
          L6event |   .0113451   .0402662     0.28   0.778    -.0677304    .0904206
          L7event |  -.0489896   .0412019    -1.19   0.235    -.1299025    .0319233
          F1event |          0  (omitted)
        ln_ew_ges |  -.1182113   .0621236    -1.90   0.058    -.2402106    .0037881
         ew_biodt |  -.0028837    .001889    -1.53   0.127    -.0065934     .000826
        ew_dtmihi |  -.0059833   .0032411    -1.85   0.065    -.0123483    .0003817
         ew_ledig |  -.0015756   .0061236    -0.26   0.797    -.0136013      .01045
       ew_married |  -.0066715   .0056744    -1.18   0.240     -.017815     .004472
        wb_anteil |   .0012735   .0017865     0.71   0.476    -.0022349    .0047819
          wb_ausl |   .0008073   .0008283     0.97   0.330    -.0008194    .0024339
         wb_18t24 |   .0006752   .0017094     0.40   0.693    -.0026816    .0040321
         wb_25t34 |    .000336   .0011827     0.28   0.776    -.0019867    .0026586
         wb_35t44 |  -.0004499   .0014673    -0.31   0.759    -.0033315    .0024317
         wb_45t59 |   .0012319   .0012374     1.00   0.320    -.0011981     .003662
          avg_dur |   .0001381   .0012342     0.11   0.911    -.0022857    .0025619
          hh_kids |   .0054809   .0022435     2.44   0.015     .0010751    .0098867
mpreis_flats_rent |  -.0006062   .0017302    -0.35   0.726     -.004004    .0027916
            _cons |   1.296955   .6016663     2.16   0.032     .1153929    2.478517
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                 
.         // PLOT: Treatment intensity
.         event_plot  treat_simple del_street_dist, ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Share reassignments""(estimates)", size(small)) xlabel(-4(1)2) xtitle(
> "Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) order(1 "Reassigned" 3 "Change in walking distance (km)" ) row(1)
>  region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel A.} Treatment Intensity",nobox span bexpand justification(left)
>  size(medium)) ///
>                 /*name(treatment, replace)*/ ) ///
>         lag_opt1(msymbol(O) msize(3pt) color(black))    lag_ci_opt1(color(black)) ///
>         lag_opt2(msymbol(O) msize(3pt) color(maroon)) lag_ci_opt2(color(maroon)) noplot savecoef

.         
.         // ---> Plot same thing with 2 yaxes for reassignments and distance (savecoef above)
.         tw  (scatter __event_coef1 __event_pos1 , sort yaxis(1) lcol(black) ms(Dh) msize(2.5pt) 
> mcol(black))   (rcap __event_lo1 __event_hi1 __event_pos1, yaxis(1) col(black)) ///
>                 (scatter __event_coef2 __event_pos2 , sort yaxis(2) lcol(maroon) ms(Dh) msize(2.
> 5pt) mcol(maroon)) (rcap __event_lo2 __event_hi2 __event_pos2, yaxis(2) col(maroon)) ///
>                 , ytitle("Share reassignments""(estimates)", size(small)) ytitle("Change in dist
> ance in km""(estimates)", axis(2) size(small) orient(rvertical) ) ///
>                 xlabel(-4(1)2) xtitle("Election since reassignment", size(medsmall)) ysc(r(-.1 .
> 85) axis(2)) ysc(r(-.1 .85) axis(1)) ylab(#6, axis(2)) ylab(#6, axis(1)) ///
>                 legend(pos(12) order(1 "Reassigned" 3 "Change in walking distance (km)" ) row(1)
>  region(style(none))) ///
>                 xline(-0.5, lcol(black) lpat(solid)) yline(0, lcolor(gray) lpat(solid)) name(tre
> atment, replace) ///
>                 title("{bf:Panel A.} Treatment Intensity",nobox span bexpand justification(left)
>  size(medium)) 

.                                 
.         // PLOT: Share Mail-in
.         event_plot  share_mail, ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(connect) ciplottype(rcap) ///
>         together  trimlead(4) trimlag(2)  noautolegend ///
>         graph_opt(ytitle("Share of mail-in votes in %""(estimates)", size(small)) xlabel(-4(1)2)
>  xtitle("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) order(1 "Share of mail-in votes in total votes" ) row(1) region(s
> tyle(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel B.} Effect on Mode of Voting" ,nobox span bexpand justification
> (left) size(medium)) ///
>                 name(share_mail,replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(black) lpat(_)) lag_ci_opt1(color(black))

.         
.         // PLOT: Turnouts Urne + Postal 
.         event_plot  turnout_urne turnout_pos_req, ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(connect) ciplottype(rcap) ///
>         together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(small)) xlabel(-4(1)2) xtitle("
> Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) order(1 "Polling place turnout" 3 "Mail-in turnout" ) row(1) regi
> on(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel C.} Effect on Mail-in and Polling Place Turnout",nobox span bex
> pand justification(left) size(medium)) ///
>                 name(urne_postal, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(navy))              lag_ci_opt1(color(navy)) ///
>         lag_opt2(msymbol(O) msize(3pt) color(maroon) lpat(-)) lag_ci_opt2(color(maroon))  

. 
.         // PLOT: Turnouts Total
.         event_plot  turnout_tot_req, ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(connect) ciplottype(rcap) ///
>         together trimlead(4) trimlag(2)  noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(small)) xlabel(-4(1)2) xtitle("
> Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) order(1 "Total turnout") row(1) region(style(none))) ///
>                 title("{bf:Panel D.} Effect on Total Turnout",nobox span bexpand justification(l
> eft) size(medium)) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 name(turnout_tot_req,replace) ) ///
>         lag_opt1(msymbol(Oh) msize(3pt) color(black))   lag_ci_opt1(color(black))        

.                 
.         * PLOT: FIGURE 7. The Effect of Reassignments on Turnout and the Mode of Voting
.         graph combine treatment share_mail urne_postal turnout_tot_req, xcommon  col(2) iscale(.
> 7)      

.         gr_edit .style.editstyle declared_ysize(4.25) editcopy  

.         graph export "$figures/Figure_7_ES_baseline.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_7_ES_b
    > aseline.pdf saved as PDF format

.         
.         
.         *** Test if post-treatment coefs for tot turnout are different
.         reghdfe turnout_tot_req F7event-L7event F1event $ctr $wgt if smpl_trim==1, absorb(i.wahl
> _id#i.stadtbez i.sb_new) cluster(sb_new)
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4395
Number of clusters (sb_new)  =        618         Root MSE        =     1.6245

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3259399    -0.25   0.800    -.7224958    .5576764
          F6event |   .2549675   .2761707     0.92   0.356    -.2873811    .7973161
          F5event |  -.2369689   .2619342    -0.90   0.366    -.7513594    .2774217
          F4event |  -.2192898   .1694872    -1.29   0.196    -.5521315     .113552
          F3event |  -.0490231   .1579758    -0.31   0.756    -.3592586    .2612124
          F2event |  -.0537065   .1322374    -0.41   0.685    -.3133964    .2059834
          L0event |  -.3892706   .1633916    -2.38   0.018    -.7101417   -.0683996
          L1event |    .011072    .200952     0.06   0.956    -.3835608    .4057048
          L2event |   .2959665    .224252     1.32   0.187    -.1444233    .7363563
          L3event |   .1243464   .2332215     0.53   0.594    -.3336578    .5823505
          L4event |   .6528057   .6207241     1.05   0.293    -.5661824    1.871794
          L5event |   1.807978   .7132727     2.53   0.011      .407241    3.208714
          L6event |   .6923315   .7952045     0.87   0.384     -.869304    2.253967
          L7event |   .4397263   1.099927     0.40   0.689    -1.720328    2.599781
          F1event |          0  (omitted)
        ln_ew_ges |   1.678109   1.068642     1.57   0.117    -.4205078    3.776725
         ew_biodt |    .761748   .0314252    24.24   0.000     .7000348    .8234612
        ew_dtmihi |  -.1673742   .0522282    -3.20   0.001    -.2699407   -.0648076
         ew_ledig |   .4238555   .0699086     6.06   0.000     .2865679    .5611432
       ew_married |   .6387186   .0688582     9.28   0.000     .5034938    .7739433
        wb_anteil |  -.5320422   .0239517   -22.21   0.000     -.579079   -.4850055
          wb_ausl |  -.0535363   .0175921    -3.04   0.002     -.088084   -.0189885
         wb_18t24 |  -.0438039    .025903    -1.69   0.091    -.0946727    .0070648
         wb_25t34 |  -.0197863   .0166674    -1.19   0.236     -.052518    .0129454
         wb_35t44 |  -.0028286   .0208991    -0.14   0.892    -.0438706    .0382134
         wb_45t59 |  -.0241236   .0196706    -1.23   0.221    -.0627531    .0145059
          avg_dur |   .0170995    .022054     0.78   0.438    -.0262106    .0604096
          hh_kids |  -.1148866    .035755    -3.21   0.001    -.1851028   -.0446704
mpreis_flats_rent |   .0148646   .0236111     0.63   0.529    -.0315033    .0612326
            _cons |   -.366715   10.03193    -0.04   0.971    -20.06759    19.33416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
.         // test if third and second post-treatment coef are different
.         test    L2event=L1event

 ( 1)  - L1event + L2event = 0

       F(  1,   617) =    2.80
            Prob > F =    0.0950

.         // test if third and second post-treatment coefs are jointly equal to zero 
.         test    (L2event=0) (L1event=0)

 ( 1)  L2event = 0
 ( 2)  L1event = 0

       F(  2,   617) =    1.55
            Prob > F =    0.2140

.         test    L2event=L1event=0

 ( 1)  - L1event + L2event = 0
 ( 2)  L2event = 0

       F(  2,   617) =    1.55
            Prob > F =    0.2140

. 
.         
. 
end of do-file
Running: 03b_main_tables_1_c1_c2_c3_c4.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
>         
> Output: Table 1, C.1, C.2, C.3, C.4
> 
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
. ********************************************************************************
. *        Prep Estimation *
. ********************************************************************************
.                         
.         // compute id for DISTANCE increase/decrease, 0 else
.         gen     tmp = (del_street_dist>0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase in event, 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease in event, 0 else"      

.         
.         // compute id for DISTANCE: 3 groups
.         cap drop tmp*

.         _pctile del_street_dist                                                 if K==0 & del_st
> reet_dist<.7, n(3)

.         gen     tmp1 = (del_street_dist>r(r2))                  if K==0
(4,664 missing values generated)

.         bys sb_new (tmp1): gen ind_dist_m3 = tmp1[1]
(2,704 missing values generated)

.         replace ind_dist_m3 = 0                                                 if missing(Ei)
(2,704 real changes made)

.         
.         gen     tmp2 = (inrange(del_street_dist,r(r1),r(r2))) if K==0
(4,664 missing values generated)

.         bys sb_new (tmp2): gen ind_dist_m2 = tmp2[1]                                            
(2,704 missing values generated)

.         replace ind_dist_m2 = 0                                                 if missing(Ei)
(2,704 real changes made)

.         
.         gen     tmp3 = (del_street_dist<r(r1))                  if K==0
(4,664 missing values generated)

.         bys sb_new (tmp3): gen ind_dist_m1 = tmp3[1]
(2,704 missing values generated)

.         replace ind_dist_m1 = 0                                                 if missing(Ei)  
>         
(2,704 real changes made)

.         
. 
. ********************************************************************************
. *        Baseline: Event Study Table (Table C1)
. ********************************************************************************
. *       
.         global order F4event F3event F2event /*F1event*/ L0event L1event L2event        

.         
.         cap program drop extract_N

.         program extract_N, rclass
  1.         /*
>                 Little program to extract and returns #precincts in control and treatm group
>                 based on e(sample)
>                 set 50 or 100 depending on whether event = 100% or 50+% reassgn.
>         
>         */
.                 syntax anything(name=treat)
  2.                 
.                 assert inlist(`treat',50,100)
  3.                         
.                 if `treat'==100 {
  4.                          distinct(sb_new) if e(sample) & K==0   // count TREAT precincts
  5.                         local T = r(ndistinct)
  6.                          distinct(sb_new) if e(sample) & K==.   // count CTRL precincts
  7.                         local C = r(ndistinct)
  8.                 }
  9.                 else {
 10.                          distinct(sb_new) if e(sample) & K50==0 // count TREAT precincts
 11.                         local T = r(ndistinct)
 12.                          distinct(sb_new) if e(sample) & K50==. // count CTRL precincts
 13.                         local C = r(ndistinct)          
 14.                 }
 15.                 
.                 return local N_T "`T'"
 16.                 return local N_C "`C'"
 17.         end

.                 
.         
.         * TABLE C1. Baseline Event Study Results
. foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         outreg, clear
  3.         
.         *>>>> gen leads and lags: treat=100%
.         cap drop L* F*
  4.         forvalues l = 7(-1)1 {
  5.                 gen     F`l'event = K==-`l'
  6.                 lab var F`l'event "Reassignment (#t-`l'#)"
  7.         }       
  8.         forvalues l = 0/7 {
  9.                 gen     L`l'event = K==`l'
 10.                 lab var L`l'event "Reassignment (#t+`l'#)"
 11.         }               
 12.         drop    F1event // drop reference period
 13.         
.                 // (1) Event study -- ELECTION-DISTRICT FE + NO Weights 
.                  reghdfe `v' F* L*                                      $ctr if smpl_trim==1    
> , absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
 14.                 extract_N 100
 15.                  outreg,  $opt  keep($order)    addrow(#treated precincts, `r(N_T)' \ #contro
> l precincts, `r(N_C)' \ Precinct FE, X \ Election-District FE, X \ Weights, )      
 16.                 
.                 // (2) Event study -- ELECTION-DISTRICT FE + WITH controls [MAIN EQ 1]
.                  reghdfe `v' F* L*                                      $ctr    if smpl_trim==1 
>         $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
 17.                 extract_N 100           
 18.                  outreg,  $opt  keep($order)    addrow(#treated precincts, `r(N_T)' \ #contro
> l precincts, `r(N_C)' \ Precinct FE, X \ Election-District FE, X \ Weights, X)                  
>    
 19. 
.                 // (3) Event study -- ELECTION-DISTRICT FE + FULL event window (not dropped)
.                  reghdfe `v' F* L*                                      $ctr  /*if smpl_trim==1*
> /       $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
 20.                 extract_N 100
 21.                  outreg,  $opt  keep($order)    addrow(#treated precincts, `r(N_T)' \ #contro
> l precincts, `r(N_C)' \ Precinct FE, X \ Election-District FE, X \ Weights, X \ Full sample, X) 
>                    
 22.                 
.                 // (4) Event study -- ONLY ELECTION FE + WITH controls [MAIN EQ 2]
.                  reghdfe `v' F* L*                                      $ctr    if smpl_trim==1 
>         $wgt, absorb(i.wahl_id i.sb_new) cluster(sb_new) 
 23.                 extract_N 100
 24.                  outreg,  $opt  keep($order)    addrow(#treated precincts, `r(N_T)' \ #contro
> l precincts, `r(N_C)' \ Precinct FE, X \ Election FE, X \ Weights, X)       
 25.                 
.                 // (5) Event study -- Eq (2) but remove CTRL precincts with some NONZERO reass a
> nd TREAT precinct with 1+ treatment
.                  reghdfe `v' F* L*                                      $ctr if cleanctr==1 & fu
> lltottreat100<=1 $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
 26.                 extract_N 100
 27.                  outreg,  $opt  keep($order)    addrow(#treated precincts, `r(N_T)' \ #contro
> l precincts, `r(N_C)' \ Precinct FE, X \ Election-District FE, X \ Weights, X \ Clean sample, X)
 28.                 
.                 // (6) Event study -- Balanced Sample (not trimmed, TREAT precincts with exactly
>  1 treatment)
.                  reghdfe `v' F* L*                                      $ctr if smpl_bal==1 & fu
> lltottreat100<=1 $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
 29.                 extract_N 100
 30.                  outreg,  $opt  keep($order)    addrow(#treated precincts, `r(N_T)' \ #contro
> l precincts, `r(N_C)' \ Precinct FE, X \ Election-District FE, X \ Weights, X \ Balanced sample,
>  X)                
 31.                 
.                 // (7) Event study -- Eq (2) but remove TREAT precincts w/ boundary change in t=
> 0
.                  reghdfe `v' F* L*                                      $ctr if sb_const_size==1
>  & smpl_trim==1 $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
 32.                 extract_N 100
 33.                  outreg,  $opt  keep($order)    addrow(#treated precincts, `r(N_T)' \ #contro
> l precincts, `r(N_C)' \ Precinct FE, X \ Election-District FE, X \ Weights, X \ No boundary chan
> ge, X)                     
 34.         
. 
.   * save
.          outreg, replay replace store(`v')
 35. }
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      17.18
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9707
                                                  Adj R-squared   =     0.9642
                                                  Within R-sq.    =     0.1622
Number of clusters (sb_new)  =        618         Root MSE        =     1.7057

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   -.114031    .362165    -0.31   0.753    -.8252565    .5971945
          F6event |     .07148   .3270374     0.22   0.827    -.5707613    .7137213
          F5event |   .2739752   .2583781     1.06   0.289     -.233432    .7813824
          F4event |   .0284906   .1736509     0.16   0.870    -.3125279     .369509
          F3event |  -.0670786   .1689615    -0.40   0.692     -.398888    .2647308
          F2event |   .0093762   .1237852     0.08   0.940    -.2337153    .2524676
          L0event |  -1.023749   .2348848    -4.36   0.000    -1.485019   -.5624781
          L1event |  -.8835828   .2358355    -3.75   0.000     -1.34672   -.4204452
          L2event |  -.7601413   .2597853    -2.93   0.004    -1.270312   -.2499706
          L3event |  -.3642244   .2709831    -1.34   0.179    -.8963854    .1679365
          L4event |  -.8610734   .4756137    -1.81   0.071    -1.795091    .0729445
          L5event |  -.5314154   .6057549    -0.88   0.381    -1.721007    .6581759
          L6event |   .9962155   .6978826     1.43   0.154    -.3742977    2.366729
          L7event |   .9206332   1.157803     0.80   0.427    -1.353079    3.194345
        ln_ew_ges |  -.5369534   .9542981    -0.56   0.574    -2.411019    1.337113
         ew_biodt |   .3724478   .0288738    12.90   0.000      .315745    .4291506
        ew_dtmihi |   .0589891   .0523562     1.13   0.260    -.0438288    .1618071
         ew_ledig |   .2051634   .0589742     3.48   0.001     .0893489    .3209778
       ew_married |   .4261879   .0601223     7.09   0.000     .3081188    .5442569
        wb_anteil |  -.2874759   .0210007   -13.69   0.000    -.3287173   -.2462344
          wb_ausl |   .0155207   .0159993     0.97   0.332    -.0158989    .0469404
         wb_18t24 |  -.0162495   .0295802    -0.55   0.583    -.0743396    .0418407
         wb_25t34 |  -.0726632   .0191975    -3.79   0.000    -.1103636   -.0349627
         wb_35t44 |  -.0013857   .0228454    -0.06   0.952    -.0462499    .0434785
         wb_45t59 |   .0116553   .0219596     0.53   0.596    -.0314694    .0547799
          avg_dur |  -.0335264   .0213128    -1.57   0.116    -.0753809    .0083281
          hh_kids |  -.0432122   .0414784    -1.04   0.298    -.1246682    .0382437
mpreis_flats_rent |   .0353665   .0257318     1.37   0.170     -.015166    .0858989
            _cons |   10.40719   8.980388     1.16   0.247    -7.228639    28.04302
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
warning: matrix in addrows option has varying size rows:
   `"R2 "',`"0.97"' \ `"N"',`"4,666"'\#treated precincts, 280 \ #control precincts, 338 \ Precinct
>  FE, X \ Election-District FE, X \ Weights, 
warning: no existing table found for merge or append

                    ---------------------------------------------------------
                                            Effect on polling place turnout 
                    ---------------------------------------------------------
                     Reassignment (#t-4#)                0.03               
                                                        (0.17)              
                     Reassignment (#t-3#)                -0.07              
                                                        (0.17)              
                     Reassignment (#t-2#)                0.01               
                                                        (0.12)              
                     Reassignment (#t+0#)              -1.02***             
                                                        (0.23)              
                     Reassignment (#t+1#)              -0.88***             
                                                        (0.24)              
                     Reassignment (#t+2#)               -0.76**             
                                                        (0.26)              
                     R2                                  0.97               
                     N                                   4,666              
                     #treated precincts                  280                
                     #control precincts                  338                
                     Precinct FE                          X                 
                     Election-District FE                 X                 
                     Weights                                                
                    ---------------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      17.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9658
                                                  Within R-sq.    =     0.1691
Number of clusters (sb_new)  =        618         Root MSE        =     1.6979

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3609916    -0.46   0.646    -.8749677    .5428746
          F6event |   .0365542   .3229698     0.11   0.910    -.5976991    .6708075
          F5event |   .2427259   .2553086     0.95   0.342    -.2586533    .7441051
          F4event |   .0132213   .1747424     0.08   0.940    -.3299406    .3563833
          F3event |  -.0565081    .169943    -0.33   0.740    -.3902449    .2772287
          F2event |    .006289   .1214372     0.05   0.959    -.2321913    .2447694
          L0event |  -.9985596   .2347416    -4.25   0.000    -1.459549   -.5375702
          L1event |   -.892731   .2344548    -3.81   0.000    -1.353157   -.4323048
          L2event |  -.7515249   .2578216    -2.91   0.004    -1.257839   -.2452106
          L3event |  -.2931705   .2663112    -1.10   0.271    -.8161567    .2298157
          L4event |  -.8782313   .4731179    -1.86   0.064    -1.807348    .0508853
          L5event |  -.5487131   .6172865    -0.89   0.374     -1.76095    .6635241
          L6event |    1.07717   .7208125     1.49   0.136    -.3383731    2.492714
          L7event |   .9427971   1.187553     0.79   0.428    -1.389339    3.274933
        ln_ew_ges |  -.7878053   .9548651    -0.83   0.410    -2.662985    1.087374
         ew_biodt |    .373603   .0281093    13.29   0.000     .3184015    .4288045
        ew_dtmihi |   .0630129   .0513196     1.23   0.220    -.0377693    .1637951
         ew_ledig |   .2127399   .0574696     3.70   0.000     .0998801    .3255996
       ew_married |   .4312672   .0590852     7.30   0.000     .3152348    .5472997
        wb_anteil |  -.2895078   .0204524   -14.16   0.000    -.3296726   -.2493431
          wb_ausl |   .0153767   .0162706     0.95   0.345    -.0165758    .0473293
         wb_18t24 |  -.0164981   .0295896    -0.56   0.577    -.0746067    .0416105
         wb_25t34 |  -.0724461   .0193717    -3.74   0.000    -.1104886   -.0344037
         wb_35t44 |    .006101   .0230068     0.27   0.791    -.0390802    .0512822
         wb_45t59 |   .0140084   .0220328     0.64   0.525      -.02926    .0572768
          avg_dur |  -.0288813   .0210954    -1.37   0.171    -.0703088    .0125462
          hh_kids |  -.0506236   .0408723    -1.24   0.216    -.1308893    .0296421
mpreis_flats_rent |   .0303932   .0255105     1.19   0.234    -.0197047    .0804911
            _cons |   11.36038   9.043266     1.26   0.210    -6.398933    29.11969
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.03                             0.01               
                                        (0.17)                           (0.17)              
     Reassignment (#t-3#)                -0.07                            -0.06              
                                        (0.17)                           (0.17)              
     Reassignment (#t-2#)                0.01                             0.01               
                                        (0.12)                           (0.12)              
     Reassignment (#t+0#)              -1.02***                         -1.00***             
                                        (0.23)                           (0.23)              
     Reassignment (#t+1#)              -0.88***                         -0.89***             
                                        (0.24)                           (0.23)              
     Reassignment (#t+2#)               -0.76**                          -0.75**             
                                        (0.26)                           (0.26)              
     R2                                  0.97                             0.97               
     N                                   4,666                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                                                                X                
    ------------------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  28,    617) =      18.66
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9711
                                                  Adj R-squared   =     0.9651
                                                  Within R-sq.    =     0.1691
Number of clusters (sb_new)  =        618         Root MSE        =     1.7175

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   -.173227   .3642418    -0.48   0.635    -.8885309     .542077
          F6event |   .0175996   .3290512     0.05   0.957    -.6285966    .6637958
          F5event |   .2430139   .2627964     0.92   0.355      -.27307    .7590978
          F4event |   .0011664   .1755472     0.01   0.995     -.343576    .3459089
          F3event |  -.0624358   .1715844    -0.36   0.716     -.399396    .2745245
          F2event |   .0148634   .1207091     0.12   0.902    -.2221871    .2519139
          L0event |  -1.021958    .232812    -4.39   0.000    -1.479158   -.5647582
          L1event |  -.8051197   .2082249    -3.87   0.000    -1.214035   -.3962042
          L2event |  -.5312147     .21963    -2.42   0.016    -.9625277   -.0999018
          L3event |   -.044022     .21733    -0.20   0.840    -.4708182    .3827741
          L4event |  -.4448824    .508537    -0.87   0.382    -1.443556    .5537908
          L5event |   .2287221   .5848142     0.39   0.696    -.9197455     1.37719
          L6event |   1.020303   .7143706     1.43   0.154    -.3825899    2.423195
          L7event |   1.549376   .6910023     2.24   0.025     .1923749    2.906378
        ln_ew_ges |  -.5573366   .8770958    -0.64   0.525    -2.279792    1.165118
         ew_biodt |   .3837705   .0268594    14.29   0.000     .3310235    .4365175
        ew_dtmihi |   .0647067   .0480135     1.35   0.178     -.029583    .1589964
         ew_ledig |    .188602   .0525267     3.59   0.000     .0854493    .2917547
       ew_married |   .3918574   .0547429     7.16   0.000     .2843524    .4993623
        wb_anteil |  -.2913202   .0200591   -14.52   0.000    -.3307126   -.2519279
          wb_ausl |   .0171239    .015327     1.12   0.264    -.0129756    .0472235
         wb_18t24 |  -.0252927   .0290219    -0.87   0.384    -.0822864    .0317011
         wb_25t34 |  -.0694945   .0194435    -3.57   0.000    -.1076778   -.0313111
         wb_35t44 |   .0099616   .0227191     0.44   0.661    -.0346545    .0545777
         wb_45t59 |   .0080387   .0212624     0.38   0.706    -.0337167    .0497942
          avg_dur |  -.0172376   .0214844    -0.80   0.423     -.059429    .0249537
          hh_kids |  -.0595004   .0401256    -1.48   0.139    -.1382997    .0192989
mpreis_flats_rent |   .0362447   .0229636     1.58   0.115    -.0088515     .081341
            _cons |   11.42872   8.437493     1.35   0.176    -5.140964    27.99841
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.03                             0.01               
                                        (0.17)                           (0.17)              
     Reassignment (#t-3#)                -0.07                            -0.06              
                                        (0.17)                           (0.17)              
     Reassignment (#t-2#)                0.01                             0.01               
                                        (0.12)                           (0.12)              
     Reassignment (#t+0#)              -1.02***                         -1.00***             
                                        (0.23)                           (0.23)              
     Reassignment (#t+1#)              -0.88***                         -0.89***             
                                        (0.24)                           (0.23)              
     Reassignment (#t+2#)               -0.76**                          -0.75**             
                                        (0.26)                           (0.26)              
     R2                                  0.97                             0.97               
     N                                   4,666                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                                                                X                
     Full sample                                                                             
    ------------------------------------------------------------------------------------------


                    ---------------------------------------------------------
                                            Effect on polling place turnout 
                    ---------------------------------------------------------
                     Reassignment (#t-4#)                0.00               
                                                        (0.18)              
                     Reassignment (#t-3#)                -0.06              
                                                        (0.17)              
                     Reassignment (#t-2#)                0.01               
                                                        (0.12)              
                     Reassignment (#t+0#)              -1.02***             
                                                        (0.23)              
                     Reassignment (#t+1#)              -0.81***             
                                                        (0.21)              
                     Reassignment (#t+2#)               -0.53*              
                                                        (0.22)              
                     R2                                  0.97               
                     N                                   4,944              
                     #treated precincts                  280                
                     #control precincts                  338                
                     Precinct FE                          X                 
                     Election-District FE                 X                 
                     Weights                              X                 
                     Full sample                           X                
                    ---------------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.69
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9633
                                                  Adj R-squared   =     0.9573
                                                  Within R-sq.    =     0.1478
Number of clusters (sb_new)  =        618         Root MSE        =     1.8963

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.2719899   .3783767    -0.72   0.473    -1.015052    .4710724
          F6event |  -.1600481   .3381397    -0.47   0.636    -.8240923    .5039962
          F5event |    .106833   .2699344     0.40   0.692    -.4232686    .6369345
          F4event |  -.1151478   .1961054    -0.59   0.557    -.5002627    .2699671
          F3event |  -.0375634   .2066195    -0.18   0.856     -.443326    .3681993
          F2event |   .1549506   .1416316     1.09   0.274    -.1231878     .433089
          L0event |  -1.068154   .2422044    -4.41   0.000    -1.543799   -.5925095
          L1event |  -.8724909   .2540048    -3.43   0.001     -1.37131   -.3736721
          L2event |  -.7015394    .270616    -2.59   0.010     -1.23298   -.1700993
          L3event |  -.0880174   .2711036    -0.32   0.746     -.620415    .4443802
          L4event |  -.1994726   .4709713    -0.42   0.672    -1.124374    .7254285
          L5event |   .4861285   .7253154     0.67   0.503    -.9382577    1.910515
          L6event |   .4275625   .8684947     0.49   0.623    -1.278002    2.133127
          L7event |   .9091168   1.138177     0.80   0.425    -1.326054    3.144287
        ln_ew_ges |  -.7462869   1.122614    -0.66   0.506    -2.950895    1.458321
         ew_biodt |   .3520456   .0289625    12.16   0.000     .2951686    .4089226
        ew_dtmihi |   .0613483   .0527968     1.16   0.246    -.0423348    .1650315
         ew_ledig |    .222933   .0597345     3.73   0.000     .1056254    .3402406
       ew_married |   .4248988    .061581     6.90   0.000      .303965    .5458326
        wb_anteil |  -.2432859   .0196388   -12.39   0.000    -.2818528   -.2047189
          wb_ausl |   .0212218   .0149923     1.42   0.157    -.0082203    .0506639
         wb_18t24 |  -.0462596   .0292086    -1.58   0.114      -.10362    .0111007
         wb_25t34 |  -.0432859   .0168545    -2.57   0.010    -.0763851   -.0101868
         wb_35t44 |    .002344   .0215326     0.11   0.913     -.039942    .0446301
         wb_45t59 |    .015456   .0219157     0.71   0.481    -.0275824    .0584944
          avg_dur |  -.0193425   .0223354    -0.87   0.387     -.063205      .02452
          hh_kids |  -.0496397   .0436321    -1.14   0.256    -.1353252    .0360458
mpreis_flats_rent |   .0512944   .0196556     2.61   0.009     .0126944    .0898944
            _cons |   8.087133   10.18894     0.79   0.428    -11.92207    28.09634
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.03                             0.01               
                                        (0.17)                           (0.17)              
     Reassignment (#t-3#)                -0.07                            -0.06              
                                        (0.17)                           (0.17)              
     Reassignment (#t-2#)                0.01                             0.01               
                                        (0.12)                           (0.12)              
     Reassignment (#t+0#)              -1.02***                         -1.00***             
                                        (0.23)                           (0.23)              
     Reassignment (#t+1#)              -0.88***                         -0.89***             
                                        (0.24)                           (0.23)              
     Reassignment (#t+2#)               -0.76**                          -0.75**             
                                        (0.26)                           (0.26)              
     R2                                  0.97                             0.97               
     N                                   4,666                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                                                                X                
     Full sample                                                                             
     Election FE                                                                             
    ------------------------------------------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.00                             -0.12              
                                        (0.18)                           (0.20)              
     Reassignment (#t-3#)                -0.06                            -0.04              
                                        (0.17)                           (0.21)              
     Reassignment (#t-2#)                0.01                             0.15               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)              -1.02***                         -1.07***             
                                        (0.23)                           (0.24)              
     Reassignment (#t+1#)              -0.81***                         -0.87***             
                                        (0.21)                           (0.25)              
     Reassignment (#t+2#)               -0.53*                           -0.70**             
                                        (0.22)                           (0.27)              
     R2                                  0.97                             0.96               
     N                                   4,944                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Weights                              X                                 X                
     Full sample                           X                                                 
     Election FE                                                           X                 
    ------------------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  28,    254) =       9.29
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9742
                                                  Adj R-squared   =     0.9662
                                                  Within R-sq.    =     0.1860
Number of clusters (sb_new)  =        255         Root MSE        =     1.6722

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.4136315   .4452807    -0.93   0.354    -1.290544     .463281
          F6event |   -.153777   .4033938    -0.38   0.703    -.9481996    .6406457
          F5event |   .1094738   .3370544     0.32   0.746    -.5543034     .773251
          F4event |   .0151536   .2400789     0.06   0.950    -.4576451    .4879524
          F3event |   -.202053   .2505077    -0.81   0.421    -.6953896    .2912836
          F2event |   .0043173   .1977896     0.02   0.983    -.3851992    .3938339
          L0event |  -1.307186   .3273057    -3.99   0.000    -1.951765   -.6626076
          L1event |  -1.489404   .3098616    -4.81   0.000    -2.099629   -.8791785
          L2event |  -1.139889    .331105    -3.44   0.001    -1.791949   -.4878278
          L3event |  -.8052141   .3628104    -2.22   0.027    -1.519714   -.0907143
          L4event |  -1.942558   .7517641    -2.58   0.010    -3.423043   -.4620738
          L5event |  -1.856974   .7597218    -2.44   0.015     -3.35313   -.3608179
          L6event |   .1480929   1.068846     0.14   0.890    -1.956836    2.253022
          L7event |   .1470455   1.346539     0.11   0.913    -2.504757    2.798848
        ln_ew_ges |  -4.714264   1.429728    -3.30   0.001    -7.529895   -1.898633
         ew_biodt |   .3286443   .0457077     7.19   0.000       .23863    .4186587
        ew_dtmihi |   .0738951   .0778352     0.95   0.343    -.0793895    .2271797
         ew_ledig |   .2096199   .0927414     2.26   0.025     .0269799    .3922599
       ew_married |   .4138027   .0946505     4.37   0.000     .2274029    .6002025
        wb_anteil |  -.2875638   .0329859    -8.72   0.000    -.3525245   -.2226031
          wb_ausl |   .0242551   .0222207     1.09   0.276    -.0195052    .0680154
         wb_18t24 |   .0025896   .0394184     0.07   0.948    -.0750389    .0802182
         wb_25t34 |  -.0602525   .0297767    -2.02   0.044    -.1188933   -.0016118
         wb_35t44 |   .0019771   .0342427     0.06   0.954    -.0654587    .0694129
         wb_45t59 |   .0143865   .0299731     0.48   0.632    -.0446409    .0734139
          avg_dur |  -.0411741   .0333756    -1.23   0.218    -.1069022    .0245541
          hh_kids |   .0611085   .0553807     1.10   0.271    -.0479553    .1701722
mpreis_flats_rent |   .0215521   .0418027     0.52   0.607    -.0607719    .1038761
            _cons |   43.24196   13.39288     3.23   0.001     16.86673    69.61719
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        840        105

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.03                             0.01               
                                        (0.17)                           (0.17)              
     Reassignment (#t-3#)                -0.07                            -0.06              
                                        (0.17)                           (0.17)              
     Reassignment (#t-2#)                0.01                             0.01               
                                        (0.12)                           (0.12)              
     Reassignment (#t+0#)              -1.02***                         -1.00***             
                                        (0.23)                           (0.23)              
     Reassignment (#t+1#)              -0.88***                         -0.89***             
                                        (0.24)                           (0.23)              
     Reassignment (#t+2#)               -0.76**                          -0.75**             
                                        (0.26)                           (0.26)              
     R2                                  0.97                             0.97               
     N                                   4,666                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                                                                X                
     Full sample                                                                             
     Election FE                                                                             
     Clean sample                                                                            
    ------------------------------------------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.00                             -0.12              
                                        (0.18)                           (0.20)              
     Reassignment (#t-3#)                -0.06                            -0.04              
                                        (0.17)                           (0.21)              
     Reassignment (#t-2#)                0.01                             0.15               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)              -1.02***                         -1.07***             
                                        (0.23)                           (0.24)              
     Reassignment (#t+1#)              -0.81***                         -0.87***             
                                        (0.21)                           (0.25)              
     Reassignment (#t+2#)               -0.53*                           -0.70**             
                                        (0.22)                           (0.27)              
     R2                                  0.97                             0.96               
     N                                   4,944                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Weights                              X                                 X                
     Full sample                           X                                                 
     Election FE                                                           X                 
     Clean sample                                                                            
    ------------------------------------------------------------------------------------------


                    ---------------------------------------------------------
                                            Effect on polling place turnout 
                    ---------------------------------------------------------
                     Reassignment (#t-4#)                0.02               
                                                        (0.24)              
                     Reassignment (#t-3#)                -0.20              
                                                        (0.25)              
                     Reassignment (#t-2#)                0.00               
                                                        (0.20)              
                     Reassignment (#t+0#)              -1.31***             
                                                        (0.33)              
                     Reassignment (#t+1#)              -1.49***             
                                                        (0.31)              
                     Reassignment (#t+2#)              -1.14***             
                                                        (0.33)              
                     R2                                  0.97               
                     N                                   2,040              
                     #treated precincts                  150                
                     #control precincts                  105                
                     Precinct FE                          X                 
                     Election-District FE                 X                 
                     Weights                              X                 
                     Full sample                                            
                     Election FE                                            
                     Clean sample                          X                
                    ---------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  22,    431) =      18.13
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9746
                                                  Adj R-squared   =     0.9687
                                                  Within R-sq.    =     0.1733
Number of clusters (sb_new)  =        432         Root MSE        =     1.6448

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |   .7022077   .4992803     1.41   0.160    -.2791194    1.683535
          F4event |   .0368231   .2462464     0.15   0.881      -.44717    .5208163
          F3event |   .0171021   .2589473     0.07   0.947    -.4918544    .5260587
          F2event |   .1914174   .2129712     0.90   0.369    -.2271741    .6100088
          L0event |  -1.568019   .3651881    -4.29   0.000    -2.285791   -.8502483
          L1event |  -1.407149   .3441269    -4.09   0.000    -2.083525   -.7307732
          L2event |  -.8044975   .3382545    -2.38   0.018    -1.469331   -.1396639
          L3event |  -.2790379    .313255    -0.89   0.374    -.8947355    .3366596
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
        ln_ew_ges |  -1.046892   1.010044    -1.04   0.301    -3.032116     .938332
         ew_biodt |   .3692613   .0301365    12.25   0.000     .3100285    .4284942
        ew_dtmihi |   .0164704   .0576431     0.29   0.775    -.0968261    .1297669
         ew_ledig |   .1765236    .069155     2.55   0.011     .0406005    .3124467
       ew_married |   .3561866   .0694503     5.13   0.000     .2196832      .49269
        wb_anteil |  -.2784894   .0218632   -12.74   0.000    -.3214612   -.2355176
          wb_ausl |   .0305717   .0168571     1.81   0.070    -.0025606     .063704
         wb_18t24 |  -.0083516    .036834    -0.23   0.821    -.0807482     .064045
         wb_25t34 |  -.0634937   .0220284    -2.88   0.004    -.1067902   -.0201971
         wb_35t44 |   .0053987   .0261243     0.21   0.836    -.0459481    .0567455
         wb_45t59 |   .0277837   .0230605     1.20   0.229    -.0175413    .0731087
          avg_dur |  -.0305989   .0219447    -1.39   0.164    -.0737307     .012533
          hh_kids |  -.0057824   .0465268    -0.12   0.901      -.09723    .0856652
mpreis_flats_rent |   .0407582   .0286592     1.42   0.156     -.015571    .0970875
            _cons |   16.41806   9.839683     1.67   0.096     -2.92167    35.75779
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.03                             0.01               
                                        (0.17)                           (0.17)              
     Reassignment (#t-3#)                -0.07                            -0.06              
                                        (0.17)                           (0.17)              
     Reassignment (#t-2#)                0.01                             0.01               
                                        (0.12)                           (0.12)              
     Reassignment (#t+0#)              -1.02***                         -1.00***             
                                        (0.23)                           (0.23)              
     Reassignment (#t+1#)              -0.88***                         -0.89***             
                                        (0.24)                           (0.23)              
     Reassignment (#t+2#)               -0.76**                          -0.75**             
                                        (0.26)                           (0.26)              
     R2                                  0.97                             0.97               
     N                                   4,666                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                                                                X                
     Full sample                                                                             
     Election FE                                                                             
     Clean sample                                                                            
     Balanced sample                                                                         
    ------------------------------------------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.00                             -0.12              
                                        (0.18)                           (0.20)              
     Reassignment (#t-3#)                -0.06                            -0.04              
                                        (0.17)                           (0.21)              
     Reassignment (#t-2#)                0.01                             0.15               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)              -1.02***                         -1.07***             
                                        (0.23)                           (0.24)              
     Reassignment (#t+1#)              -0.81***                         -0.87***             
                                        (0.21)                           (0.25)              
     Reassignment (#t+2#)               -0.53*                           -0.70**             
                                        (0.22)                           (0.27)              
     R2                                  0.97                             0.96               
     N                                   4,944                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Weights                              X                                 X                
     Full sample                           X                                                 
     Election FE                                                           X                 
     Clean sample                                                                            
     Balanced sample                                                                         
    ------------------------------------------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.02                             0.04               
                                        (0.24)                           (0.25)              
     Reassignment (#t-3#)                -0.20                            0.02               
                                        (0.25)                           (0.26)              
     Reassignment (#t-2#)                0.00                             0.19               
                                        (0.20)                           (0.21)              
     Reassignment (#t+0#)              -1.31***                         -1.57***             
                                        (0.33)                           (0.37)              
     Reassignment (#t+1#)              -1.49***                         -1.41***             
                                        (0.31)                           (0.34)              
     Reassignment (#t+2#)              -1.14***                          -0.80*              
                                        (0.33)                           (0.34)              
     R2                                  0.97                             0.97               
     N                                   2,040                            3,456              
     #treated precincts                  150                               94                
     #control precincts                  105                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                              X                                X                 
     Full sample                                                                             
     Election FE                                                                             
     Clean sample                          X                                                 
     Balanced sample                                                        X                
    ------------------------------------------------------------------------------------------

(MWFE estimator converged in 7 iterations)
note: F7event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,082
Absorbing 2 HDFE groups                           F(  27,    391) =      14.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9754
                                                  Adj R-squared   =     0.9692
                                                  Within R-sq.    =     0.1757
Number of clusters (sb_new)  =        392         Root MSE        =     1.6316

                                    (Std. err. adjusted for 392 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |   .6030384   .5840476     1.03   0.302    -.5452281    1.751305
          F5event |   .6949401   .4984432     1.39   0.164     -.285024    1.674904
          F4event |   .4994571   .4971666     1.00   0.316    -.4779971    1.476911
          F3event |   .6250212   .5153779     1.21   0.226    -.3882374     1.63828
          F2event |   .4832032   .4167672     1.16   0.247    -.3361819    1.302588
          L0event |  -1.365281   .4226761    -3.23   0.001    -2.196284    -.534279
          L1event |  -1.084039   .4853274    -2.23   0.026    -2.038217   -.1298615
          L2event |  -.8883029   .5438781    -1.63   0.103    -1.957594    .1809884
          L3event |  -.1350272   .5932953    -0.23   0.820    -1.301475    1.031421
          L4event |  -.3727826   .6597837    -0.57   0.572     -1.66995     .924385
          L5event |  -.3775416    .852433    -0.44   0.658    -2.053467    1.298384
          L6event |   .1523441   .6765322     0.23   0.822    -1.177752     1.48244
          L7event |   4.231003   .7855763     5.39   0.000     2.686521    5.775485
        ln_ew_ges |  -.7573187   1.071088    -0.71   0.480    -2.863131    1.348494
         ew_biodt |   .3912785   .0328559    11.91   0.000     .3266822    .4558749
        ew_dtmihi |   .0808793   .0595173     1.36   0.175    -.0361346    .1978932
         ew_ledig |   .2180964   .0687697     3.17   0.002     .0828918    .3533009
       ew_married |   .3726404   .0703067     5.30   0.000     .2344139     .510867
        wb_anteil |  -.2707168   .0258754   -10.46   0.000    -.3215892   -.2198443
          wb_ausl |   .0280459   .0199383     1.41   0.160    -.0111538    .0672457
         wb_18t24 |  -.0050661   .0378203    -0.13   0.894    -.0794227    .0692905
         wb_25t34 |  -.0502675   .0226667    -2.22   0.027    -.0948314   -.0057037
         wb_35t44 |  -.0274858   .0271376    -1.01   0.312    -.0808397    .0258682
         wb_45t59 |   .0375158   .0256343     1.46   0.144    -.0128825    .0879141
          avg_dur |  -.0380138   .0229339    -1.66   0.098    -.0831029    .0070753
          hh_kids |  -.0166629    .047738    -0.35   0.727    -.1105182    .0771925
mpreis_flats_rent |   .0526244   .0301114     1.75   0.081    -.0065761    .1118249
            _cons |   9.098269   10.11606     0.90   0.369     -10.7904    28.98694
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       392         392           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         54         54

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.03                             0.01               
                                        (0.17)                           (0.17)              
     Reassignment (#t-3#)                -0.07                            -0.06              
                                        (0.17)                           (0.17)              
     Reassignment (#t-2#)                0.01                             0.01               
                                        (0.12)                           (0.12)              
     Reassignment (#t+0#)              -1.02***                         -1.00***             
                                        (0.23)                           (0.23)              
     Reassignment (#t+1#)              -0.88***                         -0.89***             
                                        (0.24)                           (0.23)              
     Reassignment (#t+2#)               -0.76**                          -0.75**             
                                        (0.26)                           (0.26)              
     R2                                  0.97                             0.97               
     N                                   4,666                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                                                                X                
     Full sample                                                                             
     Election FE                                                                             
     Clean sample                                                                            
     Balanced sample                                                                         
     No boundary change                                                                      
    ------------------------------------------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.00                             -0.12              
                                        (0.18)                           (0.20)              
     Reassignment (#t-3#)                -0.06                            -0.04              
                                        (0.17)                           (0.21)              
     Reassignment (#t-2#)                0.01                             0.15               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)              -1.02***                         -1.07***             
                                        (0.23)                           (0.24)              
     Reassignment (#t+1#)              -0.81***                         -0.87***             
                                        (0.21)                           (0.25)              
     Reassignment (#t+2#)               -0.53*                           -0.70**             
                                        (0.22)                           (0.27)              
     R2                                  0.97                             0.96               
     N                                   4,944                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Weights                              X                                 X                
     Full sample                           X                                                 
     Election FE                                                           X                 
     Clean sample                                                                            
     Balanced sample                                                                         
     No boundary change                                                                      
    ------------------------------------------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.02                             0.04               
                                        (0.24)                           (0.25)              
     Reassignment (#t-3#)                -0.20                            0.02               
                                        (0.25)                           (0.26)              
     Reassignment (#t-2#)                0.00                             0.19               
                                        (0.20)                           (0.21)              
     Reassignment (#t+0#)              -1.31***                         -1.57***             
                                        (0.33)                           (0.37)              
     Reassignment (#t+1#)              -1.49***                         -1.41***             
                                        (0.31)                           (0.34)              
     Reassignment (#t+2#)              -1.14***                          -0.80*              
                                        (0.33)                           (0.34)              
     R2                                  0.97                             0.97               
     N                                   2,040                            3,456              
     #treated precincts                  150                               94                
     #control precincts                  105                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                              X                                X                 
     Full sample                                                                             
     Election FE                                                                             
     Clean sample                          X                                                 
     Balanced sample                                                        X                
     No boundary change                                                                      
    ------------------------------------------------------------------------------------------


                    ---------------------------------------------------------
                                            Effect on polling place turnout 
                    ---------------------------------------------------------
                     Reassignment (#t-4#)                0.50               
                                                        (0.50)              
                     Reassignment (#t-3#)                0.63               
                                                        (0.52)              
                     Reassignment (#t-2#)                0.48               
                                                        (0.42)              
                     Reassignment (#t+0#)               -1.37**             
                                                        (0.42)              
                     Reassignment (#t+1#)               -1.08*              
                                                        (0.49)              
                     Reassignment (#t+2#)                -0.89              
                                                        (0.54)              
                     R2                                  0.98               
                     N                                   3,082              
                     #treated precincts                   54                
                     #control precincts                  338                
                     Precinct FE                          X                 
                     Election-District FE                 X                 
                     Weights                              X                 
                     Full sample                                            
                     Election FE                                            
                     Clean sample                                           
                     Balanced sample                                        
                     No boundary change                    X                
                    ---------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.03                             0.01               
                                        (0.17)                           (0.17)              
     Reassignment (#t-3#)                -0.07                            -0.06              
                                        (0.17)                           (0.17)              
     Reassignment (#t-2#)                0.01                             0.01               
                                        (0.12)                           (0.12)              
     Reassignment (#t+0#)              -1.02***                         -1.00***             
                                        (0.23)                           (0.23)              
     Reassignment (#t+1#)              -0.88***                         -0.89***             
                                        (0.24)                           (0.23)              
     Reassignment (#t+2#)               -0.76**                          -0.75**             
                                        (0.26)                           (0.26)              
     R2                                  0.97                             0.97               
     N                                   4,666                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                                                                X                
     Full sample                                                                             
     Election FE                                                                             
     Clean sample                                                                            
     Balanced sample                                                                         
     No boundary change                                                                      
    ------------------------------------------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.00                             -0.12              
                                        (0.18)                           (0.20)              
     Reassignment (#t-3#)                -0.06                            -0.04              
                                        (0.17)                           (0.21)              
     Reassignment (#t-2#)                0.01                             0.15               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)              -1.02***                         -1.07***             
                                        (0.23)                           (0.24)              
     Reassignment (#t+1#)              -0.81***                         -0.87***             
                                        (0.21)                           (0.25)              
     Reassignment (#t+2#)               -0.53*                           -0.70**             
                                        (0.22)                           (0.27)              
     R2                                  0.97                             0.96               
     N                                   4,944                            4,666              
     #treated precincts                  280                              280                
     #control precincts                  338                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Weights                              X                                 X                
     Full sample                           X                                                 
     Election FE                                                           X                 
     Clean sample                                                                            
     Balanced sample                                                                         
     No boundary change                                                                      
    ------------------------------------------------------------------------------------------


    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Reassignment (#t-4#)                0.02                             0.04               
                                        (0.24)                           (0.25)              
     Reassignment (#t-3#)                -0.20                            0.02               
                                        (0.25)                           (0.26)              
     Reassignment (#t-2#)                0.00                             0.19               
                                        (0.20)                           (0.21)              
     Reassignment (#t+0#)              -1.31***                         -1.57***             
                                        (0.33)                           (0.37)              
     Reassignment (#t+1#)              -1.49***                         -1.41***             
                                        (0.31)                           (0.34)              
     Reassignment (#t+2#)              -1.14***                          -0.80*              
                                        (0.33)                           (0.34)              
     R2                                  0.97                             0.97               
     N                                   2,040                            3,456              
     #treated precincts                  150                               94                
     #control precincts                  105                              338                
     Precinct FE                          X                                X                 
     Election-District FE                 X                                X                 
     Weights                              X                                X                 
     Full sample                                                                             
     Election FE                                                                             
     Clean sample                          X                                                 
     Balanced sample                                                        X                
     No boundary change                                                                      
    ------------------------------------------------------------------------------------------


                    ---------------------------------------------------------
                                            Effect on polling place turnout 
                    ---------------------------------------------------------
                     Reassignment (#t-4#)                0.50               
                                                        (0.50)              
                     Reassignment (#t-3#)                0.63               
                                                        (0.52)              
                     Reassignment (#t-2#)                0.48               
                                                        (0.42)              
                     Reassignment (#t+0#)               -1.37**             
                                                        (0.42)              
                     Reassignment (#t+1#)               -1.08*              
                                                        (0.49)              
                     Reassignment (#t+2#)                -0.89              
                                                        (0.54)              
                     R2                                  0.98               
                     N                                   3,082              
                     #treated precincts                   54                
                     #control precincts                  338                
                     Precinct FE                          X                 
                     Election-District FE                 X                 
                     Weights                              X                 
                     Full sample                                            
                     Election FE                                            
                     Clean sample                                           
                     Balanced sample                                        
                     No boundary change                    X                
                    ---------------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.81
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9611
                                                  Adj R-squared   =     0.9525
                                                  Within R-sq.    =     0.1970
Number of clusters (sb_new)  =        618         Root MSE        =     1.6774

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0301026   .3319763     0.09   0.928    -.6218378     .682043
          F6event |   .1654923   .2593685     0.64   0.524    -.3438598    .6748445
          F5event |  -.5184023   .2593943    -2.00   0.046    -1.027805   -.0089996
          F4event |  -.2409661   .1595755    -1.51   0.132    -.5543431     .072411
          F3event |    .016439    .150253     0.11   0.913    -.2786303    .3115082
          F2event |  -.0722794    .124265    -0.58   0.561    -.3163129    .1717542
          L0event |   .6619609   .2192001     3.02   0.003     .2314923     1.09243
          L1event |   .9273297   .2275654     4.08   0.000     .4804332    1.374226
          L2event |   1.068598   .2627929     4.07   0.000     .5525214    1.584675
          L3event |   .4560134   .2591491     1.76   0.079    -.0529078    .9649346
          L4event |   1.529996   .6475836     2.36   0.018     .2582607    2.801731
          L5event |    2.40259   .5471744     4.39   0.000      1.32804     3.47714
          L6event |  -.2973649   .8796689    -0.34   0.735    -2.024873    1.430143
          L7event |  -.3930196   .7644688    -0.51   0.607    -1.894296    1.108257
        ln_ew_ges |   2.461905   1.468826     1.68   0.094    -.4225993     5.34641
         ew_biodt |   .3783881   .0306647    12.34   0.000     .3181683    .4386078
        ew_dtmihi |   -.236338   .0603057    -3.92   0.000    -.3547673   -.1179087
         ew_ledig |   .2257843   .0830987     2.72   0.007     .0625936     .388975
       ew_married |   .2173769   .0826594     2.63   0.009      .055049    .3797048
        wb_anteil |  -.2313154   .0239362    -9.66   0.000    -.2783217   -.1843091
          wb_ausl |  -.0684902   .0144269    -4.75   0.000    -.0968219   -.0401585
         wb_18t24 |  -.0334443   .0273422    -1.22   0.222    -.0871393    .0202507
         wb_25t34 |   .0542423   .0193899     2.80   0.005     .0161641    .0923206
         wb_35t44 |  -.0036156   .0243962    -0.15   0.882    -.0515251     .044294
         wb_45t59 |  -.0359231   .0201943    -1.78   0.076    -.0755811    .0037348
          avg_dur |   .0500061    .023762     2.10   0.036      .003342    .0966703
          hh_kids |  -.0639902   .0424534    -1.51   0.132    -.1473609    .0193805
mpreis_flats_rent |  -.0194738   .0231698    -0.84   0.401    -.0649751    .0260275
            _cons |   -12.9461   12.02985    -1.08   0.282    -36.57051    10.67831
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
warning: matrix in addrows option has varying size rows:
   `"R2 "',`"0.96"' \ `"N"',`"4,666"'\#treated precincts, 280 \ #control precincts, 338 \ Precinct
>  FE, X \ Election-District FE, X \ Weights, 
warning: no existing table found for merge or append

                       ---------------------------------------------------
                                               Effect on mail-in turnout 
                       ---------------------------------------------------
                        Reassignment (#t-4#)             -0.24           
                                                        (0.16)           
                        Reassignment (#t-3#)             0.02            
                                                        (0.15)           
                        Reassignment (#t-2#)             -0.07           
                                                        (0.12)           
                        Reassignment (#t+0#)            0.66**           
                                                        (0.22)           
                        Reassignment (#t+1#)            0.93***          
                                                        (0.23)           
                        Reassignment (#t+2#)            1.07***          
                                                        (0.26)           
                        R2                               0.96            
                        N                                4,666           
                        #treated precincts               280             
                        #control precincts               338             
                        Precinct FE                       X              
                        Election-District FE              X              
                        Weights                                          
                       ---------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9616
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2040
Number of clusters (sb_new)  =        618         Root MSE        =     1.6847

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3314703     0.25   0.801    -.5673098    .7345839
          F6event |   .2184141   .2600152     0.84   0.401    -.2922081    .7290362
          F5event |  -.4796951   .2637694    -1.82   0.069    -.9976898    .0382995
          F4event |  -.2325114   .1614186    -1.44   0.150    -.5495078     .084485
          F3event |   .0074853   .1524588     0.05   0.961    -.2919157    .3068862
          F2event |   -.059996   .1248273    -0.48   0.631    -.3051339    .1851419
          L0event |   .6092887   .2189284     2.78   0.006     .1793535    1.039224
          L1event |   .9038027   .2273231     3.98   0.000      .457382    1.350223
          L2event |   1.047491   .2631015     3.98   0.000     .5308081    1.564174
          L3event |   .4175165   .2577372     1.62   0.106    -.0886321    .9236651
          L4event |   1.531036   .6502778     2.35   0.019     .2540104    2.808063
          L5event |   2.356691   .5459981     4.32   0.000     1.284451    3.428931
          L6event |  -.3848372    .877942    -0.44   0.661    -2.108954     1.33928
          L7event |   -.503072    .765562    -0.66   0.511    -2.006495    1.000351
        ln_ew_ges |   2.465914   1.349016     1.83   0.068    -.1833058    5.115133
         ew_biodt |    .388145   .0296323    13.10   0.000     .3299526    .4463375
        ew_dtmihi |  -.2303872   .0595829    -3.87   0.000     -.347397   -.1133774
         ew_ledig |   .2111155   .0802108     2.63   0.009     .0535962    .3686348
       ew_married |   .2074512   .0799261     2.60   0.010     .0504911    .3644114
        wb_anteil |  -.2425344   .0223631   -10.85   0.000    -.2864513   -.1986174
          wb_ausl |   -.068913   .0145836    -4.73   0.000    -.0975526   -.0402734
         wb_18t24 |  -.0273058   .0275422    -0.99   0.322    -.0813937    .0267821
         wb_25t34 |   .0526598   .0194716     2.70   0.007     .0144212    .0908985
         wb_35t44 |  -.0089296   .0249211    -0.36   0.720    -.0578702    .0400109
         wb_45t59 |   -.038132   .0205362    -1.86   0.064    -.0784614    .0021973
          avg_dur |   .0459808   .0233899     1.97   0.050     .0000472    .0919144
          hh_kids |  -.0642629   .0417916    -1.54   0.125    -.1463339    .0178081
mpreis_flats_rent |  -.0155286   .0234069    -0.66   0.507    -.0614954    .0304383
            _cons |  -11.72708   11.35784    -1.03   0.302     -34.0318    10.57764
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.24                      -0.23           
                                           (0.16)                     (0.16)           
           Reassignment (#t-3#)             0.02                       0.01            
                                           (0.15)                     (0.15)           
           Reassignment (#t-2#)             -0.07                      -0.06           
                                           (0.12)                     (0.12)           
           Reassignment (#t+0#)            0.66**                     0.61**           
                                           (0.22)                     (0.22)           
           Reassignment (#t+1#)            0.93***                    0.90***          
                                           (0.23)                     (0.23)           
           Reassignment (#t+2#)            1.07***                    1.05***          
                                           (0.26)                     (0.26)           
           R2                               0.96                       0.96            
           N                                4,666                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                                                       X             
          ------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  28,    617) =      18.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9596
                                                  Adj R-squared   =     0.9513
                                                  Within R-sq.    =     0.2057
Number of clusters (sb_new)  =        618         Root MSE        =     1.7040

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .1066465   .3284978     0.32   0.746    -.5384629    .7517559
          F6event |   .2461815   .2602396     0.95   0.345    -.2648812    .7572442
          F5event |  -.4512395   .2665603    -1.69   0.091     -.974715    .0722359
          F4event |  -.2120684   .1591433    -1.33   0.183    -.5245967    .1004599
          F3event |   .0174739   .1526192     0.11   0.909    -.2822421    .3171899
          F2event |  -.0530192   .1250189    -0.42   0.672    -.2985332    .1924949
          L0event |   .6185535   .2181973     2.83   0.005     .1900542    1.047053
          L1event |   .7284998   .2072658     3.51   0.000     .3214678    1.135532
          L2event |   .7125749   .2310569     3.08   0.002     .2588216    1.166328
          L3event |   .2145545   .2331457     0.92   0.358    -.2433009    .6724098
          L4event |   1.331716   .5283797     2.52   0.012      .294075    2.369356
          L5event |   1.330146   .5376169     2.47   0.014     .2743656    2.385927
          L6event |   .3169248   .8422598     0.38   0.707    -1.337119    1.970968
          L7event |  -.1434691   .7469971    -0.19   0.848    -1.610434    1.323496
        ln_ew_ges |   2.267182   1.102914     2.06   0.040     .1012625    4.433102
         ew_biodt |   .3904263   .0285812    13.66   0.000     .3342981    .4465546
        ew_dtmihi |  -.2157693   .0560829    -3.85   0.000    -.3259058   -.1056327
         ew_ledig |   .2358741   .0632657     3.73   0.000      .111632    .3601163
       ew_married |   .2674911   .0641261     4.17   0.000     .1415592    .3934231
        wb_anteil |  -.2502606   .0226843   -11.03   0.000    -.2948083   -.2057128
          wb_ausl |  -.0763807   .0147058    -5.19   0.000    -.1052602   -.0475012
         wb_18t24 |  -.0109251   .0264562    -0.41   0.680    -.0628802    .0410299
         wb_25t34 |   .0530783   .0186129     2.85   0.004      .016526    .0896306
         wb_35t44 |  -.0153762   .0240818    -0.64   0.523    -.0626685    .0319161
         wb_45t59 |  -.0394036   .0196736    -2.00   0.046    -.0780389   -.0007684
          avg_dur |   .0340954   .0230445     1.48   0.140    -.0111598    .0793507
          hh_kids |  -.0671418    .039079    -1.72   0.086    -.1438857    .0096022
mpreis_flats_rent |  -.0121587   .0218851    -0.56   0.579    -.0551371    .0308197
            _cons |  -12.88857   9.565169    -1.35   0.178     -31.6728    5.895662
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.24                      -0.23           
                                           (0.16)                     (0.16)           
           Reassignment (#t-3#)             0.02                       0.01            
                                           (0.15)                     (0.15)           
           Reassignment (#t-2#)             -0.07                      -0.06           
                                           (0.12)                     (0.12)           
           Reassignment (#t+0#)            0.66**                     0.61**           
                                           (0.22)                     (0.22)           
           Reassignment (#t+1#)            0.93***                    0.90***          
                                           (0.23)                     (0.23)           
           Reassignment (#t+2#)            1.07***                    1.05***          
                                           (0.26)                     (0.26)           
           R2                               0.96                       0.96            
           N                                4,666                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                                                       X             
           Full sample                                                                 
          ------------------------------------------------------------------------------


                       ---------------------------------------------------
                                               Effect on mail-in turnout 
                       ---------------------------------------------------
                        Reassignment (#t-4#)             -0.21           
                                                        (0.16)           
                        Reassignment (#t-3#)             0.02            
                                                        (0.15)           
                        Reassignment (#t-2#)             -0.05           
                                                        (0.13)           
                        Reassignment (#t+0#)            0.62**           
                                                        (0.22)           
                        Reassignment (#t+1#)            0.73***          
                                                        (0.21)           
                        Reassignment (#t+2#)            0.71**           
                                                        (0.23)           
                        R2                               0.96            
                        N                                4,944           
                        #treated precincts               280             
                        #control precincts               338             
                        Precinct FE                       X              
                        Election-District FE              X              
                        Weights                           X              
                        Full sample                        X             
                       ---------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      15.68
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9457
                                                  Adj R-squared   =     0.9369
                                                  Within R-sq.    =     0.2003
Number of clusters (sb_new)  =        618         Root MSE        =     1.9547

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .276988   .3092727     0.90   0.371    -.3303667    .8843427
          F6event |   .3148414   .2770009     1.14   0.256    -.2291375    .8588203
          F5event |  -.3360842   .2820305    -1.19   0.234    -.8899403    .2177719
          F4event |  -.1089699   .1694836    -0.64   0.520    -.4418045    .2238647
          F3event |  -.1143589   .2016849    -0.57   0.571    -.5104309    .2817132
          F2event |  -.1662226   .1429373    -1.16   0.245    -.4469252    .1144801
          L0event |   .5381031   .2339631     2.30   0.022     .0786427    .9975636
          L1event |   .8706087   .2415275     3.60   0.000     .3962931    1.344924
          L2event |   .9679545   .2802916     3.45   0.001     .4175133    1.518396
          L3event |   .0769893    .269978     0.29   0.776    -.4531979    .6071765
          L4event |   1.629122   .6201424     2.63   0.009     .4112765    2.846968
          L5event |   .9910967   .5551212     1.79   0.075    -.0990593    2.081253
          L6event |   .0056946   .5736399     0.01   0.992    -1.120829    1.132218
          L7event |   -.400396   .9271444    -0.43   0.666    -2.221137    1.420345
        ln_ew_ges |   2.396505   1.412975     1.70   0.090    -.3783192    5.171328
         ew_biodt |   .4381682   .0312317    14.03   0.000     .3768348    .4995016
        ew_dtmihi |  -.2278822    .060953    -3.74   0.000    -.3475827   -.1081817
         ew_ledig |     .16008   .0853711     1.88   0.061    -.0075731     .327733
       ew_married |   .1907753   .0848466     2.25   0.025     .0241523    .3573984
        wb_anteil |  -.2980442    .026516   -11.24   0.000    -.3501167   -.2459717
          wb_ausl |   -.056026   .0139204    -4.02   0.000    -.0833631    -.028689
         wb_18t24 |  -.0102823   .0285045    -0.36   0.718    -.0662599    .0456953
         wb_25t34 |   .0315455   .0168646     1.87   0.062    -.0015734    .0646644
         wb_35t44 |  -.0088774   .0224818    -0.39   0.693    -.0530276    .0352729
         wb_45t59 |   -.027485   .0205433    -1.34   0.181    -.0678283    .0128583
          avg_dur |   .0609851   .0243414     2.51   0.012      .013183    .1087872
          hh_kids |  -.0405334   .0434366    -0.93   0.351    -.1258349    .0447681
mpreis_flats_rent |   .0523958   .0213153     2.46   0.014     .0105364    .0942552
            _cons |  -9.380828    12.1834    -0.77   0.442    -33.30678    14.54513
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.24                      -0.23           
                                           (0.16)                     (0.16)           
           Reassignment (#t-3#)             0.02                       0.01            
                                           (0.15)                     (0.15)           
           Reassignment (#t-2#)             -0.07                      -0.06           
                                           (0.12)                     (0.12)           
           Reassignment (#t+0#)            0.66**                     0.61**           
                                           (0.22)                     (0.22)           
           Reassignment (#t+1#)            0.93***                    0.90***          
                                           (0.23)                     (0.23)           
           Reassignment (#t+2#)            1.07***                    1.05***          
                                           (0.26)                     (0.26)           
           R2                               0.96                       0.96            
           N                                4,666                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                                                       X             
           Full sample                                                                 
           Election FE                                                                 
          ------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.21                      -0.11           
                                           (0.16)                     (0.17)           
           Reassignment (#t-3#)             0.02                       -0.11           
                                           (0.15)                     (0.20)           
           Reassignment (#t-2#)             -0.05                      -0.17           
                                           (0.13)                     (0.14)           
           Reassignment (#t+0#)            0.62**                      0.54*           
                                           (0.22)                     (0.23)           
           Reassignment (#t+1#)            0.73***                    0.87***          
                                           (0.21)                     (0.24)           
           Reassignment (#t+2#)            0.71**                     0.97***          
                                           (0.23)                     (0.28)           
           R2                               0.96                       0.95            
           N                                4,944                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                                         
           Weights                           X                           X             
           Full sample                        X                                        
           Election FE                                                  X              
          ------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  28,    254) =      10.44
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9656
                                                  Adj R-squared   =     0.9549
                                                  Within R-sq.    =     0.2410
Number of clusters (sb_new)  =        255         Root MSE        =     1.6412

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0343475   .4407511     0.08   0.938    -.8336446    .9023396
          F6event |   .2838041   .3501287     0.81   0.418     -.405721    .9733293
          F5event |  -.6083014      .3689    -1.65   0.100    -1.334794    .1181908
          F4event |  -.3061884   .2476648    -1.24   0.217    -.7939265    .1815496
          F3event |   .0806786   .2158862     0.37   0.709    -.3444763    .5058335
          F2event |  -.1458334   .1824852    -0.80   0.425    -.5052102    .2135435
          L0event |   .6623004   .2936698     2.26   0.025     .0839624    1.240638
          L1event |   1.204526   .2962957     4.07   0.000     .6210166    1.788035
          L2event |   1.268881   .3503079     3.62   0.000     .5790026    1.958759
          L3event |   .8993154   .3514284     2.56   0.011     .2072308      1.5914
          L4event |   1.728528    1.02429     1.69   0.093    -.2886553    3.745711
          L5event |   3.075664   .7540532     4.08   0.000     1.590671    4.560657
          L6event |   .1949674   1.293719     0.15   0.880    -2.352815    2.742749
          L7event |   .4098224   1.056839     0.39   0.699    -1.671461    2.491106
        ln_ew_ges |    6.22054   1.636346     3.80   0.000     2.998006    9.443074
         ew_biodt |   .3970154   .0443591     8.95   0.000     .3096569    .4843739
        ew_dtmihi |  -.1862057   .0757348    -2.46   0.015    -.3353539   -.0370574
         ew_ledig |   .2570122   .0980706     2.62   0.009     .0638771    .4501473
       ew_married |   .3406621   .1049164     3.25   0.001     .1340452    .5472789
        wb_anteil |  -.2302129   .0295893    -7.78   0.000    -.2884845   -.1719413
          wb_ausl |  -.0800943   .0200217    -4.00   0.000    -.1195239   -.0406647
         wb_18t24 |  -.0555542   .0401755    -1.38   0.168    -.1346737    .0235652
         wb_25t34 |   .0158594   .0273901     0.58   0.563    -.0380813    .0698001
         wb_35t44 |  -.0287523   .0360464    -0.80   0.426    -.0997401    .0422355
         wb_45t59 |  -.0347805   .0280105    -1.24   0.215    -.0899429    .0203819
          avg_dur |   .0300947   .0316257     0.95   0.342    -.0321873    .0923767
          hh_kids |  -.1857129   .0573798    -3.24   0.001    -.2987137   -.0727121
mpreis_flats_rent |  -.0314947   .0398013    -0.79   0.430    -.1098772    .0468879
            _cons |  -45.65497   15.01959    -3.04   0.003    -75.23377   -16.07617
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        840        105

          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.24                      -0.23           
                                           (0.16)                     (0.16)           
           Reassignment (#t-3#)             0.02                       0.01            
                                           (0.15)                     (0.15)           
           Reassignment (#t-2#)             -0.07                      -0.06           
                                           (0.12)                     (0.12)           
           Reassignment (#t+0#)            0.66**                     0.61**           
                                           (0.22)                     (0.22)           
           Reassignment (#t+1#)            0.93***                    0.90***          
                                           (0.23)                     (0.23)           
           Reassignment (#t+2#)            1.07***                    1.05***          
                                           (0.26)                     (0.26)           
           R2                               0.96                       0.96            
           N                                4,666                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                                                       X             
           Full sample                                                                 
           Election FE                                                                 
           Clean sample                                                                
          ------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.21                      -0.11           
                                           (0.16)                     (0.17)           
           Reassignment (#t-3#)             0.02                       -0.11           
                                           (0.15)                     (0.20)           
           Reassignment (#t-2#)             -0.05                      -0.17           
                                           (0.13)                     (0.14)           
           Reassignment (#t+0#)            0.62**                      0.54*           
                                           (0.22)                     (0.23)           
           Reassignment (#t+1#)            0.73***                    0.87***          
                                           (0.21)                     (0.24)           
           Reassignment (#t+2#)            0.71**                     0.97***          
                                           (0.23)                     (0.28)           
           R2                               0.96                       0.95            
           N                                4,944                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                                         
           Weights                           X                           X             
           Full sample                        X                                        
           Election FE                                                  X              
           Clean sample                                                                
          ------------------------------------------------------------------------------


                       ---------------------------------------------------
                                               Effect on mail-in turnout 
                       ---------------------------------------------------
                        Reassignment (#t-4#)             -0.31           
                                                        (0.25)           
                        Reassignment (#t-3#)             0.08            
                                                        (0.22)           
                        Reassignment (#t-2#)             -0.15           
                                                        (0.18)           
                        Reassignment (#t+0#)             0.66*           
                                                        (0.29)           
                        Reassignment (#t+1#)            1.20***          
                                                        (0.30)           
                        Reassignment (#t+2#)            1.27***          
                                                        (0.35)           
                        R2                               0.97            
                        N                                2,040           
                        #treated precincts               150             
                        #control precincts               105             
                        Precinct FE                       X              
                        Election-District FE              X              
                        Weights                           X              
                        Full sample                                      
                        Election FE                                      
                        Clean sample                       X             
                       ---------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  22,    431) =      20.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9629
                                                  Adj R-squared   =     0.9542
                                                  Within R-sq.    =     0.2424
Number of clusters (sb_new)  =        432         Root MSE        =     1.6396

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |  -.3177852   .2783712    -1.14   0.254    -.8649192    .2293488
          F4event |  -.1846473   .1853606    -1.00   0.320    -.5489704    .1796757
          F3event |  -.0441353   .2410251    -0.18   0.855    -.5178662    .4295956
          F2event |  -.2318892   .1801612    -1.29   0.199     -.585993    .1222146
          L0event |   1.316149   .3145579     4.18   0.000      .697891    1.934408
          L1event |   1.243357   .3181669     3.91   0.000     .6180051    1.868709
          L2event |   1.276413   .3462644     3.69   0.000     .5958365     1.95699
          L3event |   .3309612   .3099105     1.07   0.286    -.2781626     .940085
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
        ln_ew_ges |   2.196377   1.367522     1.61   0.109    -.4914638    4.884218
         ew_biodt |   .4087621   .0312801    13.07   0.000     .3472817    .4702425
        ew_dtmihi |  -.1724686     .06589    -2.62   0.009    -.3019743   -.0429628
         ew_ledig |   .3139282    .081657     3.84   0.000     .1534327    .4744237
       ew_married |   .3344284   .0810542     4.13   0.000     .1751178     .493739
        wb_anteil |  -.2547165   .0240391   -10.60   0.000     -.301965    -.207468
          wb_ausl |  -.0714395   .0161751    -4.42   0.000    -.1032313   -.0396477
         wb_18t24 |  -.0376454   .0340422    -1.11   0.269    -.1045548    .0292641
         wb_25t34 |   .0468778   .0221855     2.11   0.035     .0032725    .0904831
         wb_35t44 |  -.0270654    .028017    -0.97   0.335    -.0821322    .0280015
         wb_45t59 |  -.0476295   .0222632    -2.14   0.033    -.0913875   -.0038715
          avg_dur |   .0338776    .025203     1.34   0.180    -.0156585    .0834138
          hh_kids |   -.091501   .0517309    -1.77   0.078    -.1931772    .0101753
mpreis_flats_rent |  -.0158303     .02821    -0.56   0.575    -.0712765    .0396159
            _cons |  -19.23747   12.24348    -1.57   0.117    -43.30182    4.826886
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.24                      -0.23           
                                           (0.16)                     (0.16)           
           Reassignment (#t-3#)             0.02                       0.01            
                                           (0.15)                     (0.15)           
           Reassignment (#t-2#)             -0.07                      -0.06           
                                           (0.12)                     (0.12)           
           Reassignment (#t+0#)            0.66**                     0.61**           
                                           (0.22)                     (0.22)           
           Reassignment (#t+1#)            0.93***                    0.90***          
                                           (0.23)                     (0.23)           
           Reassignment (#t+2#)            1.07***                    1.05***          
                                           (0.26)                     (0.26)           
           R2                               0.96                       0.96            
           N                                4,666                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                                                       X             
           Full sample                                                                 
           Election FE                                                                 
           Clean sample                                                                
           Balanced sample                                                             
          ------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.21                      -0.11           
                                           (0.16)                     (0.17)           
           Reassignment (#t-3#)             0.02                       -0.11           
                                           (0.15)                     (0.20)           
           Reassignment (#t-2#)             -0.05                      -0.17           
                                           (0.13)                     (0.14)           
           Reassignment (#t+0#)            0.62**                      0.54*           
                                           (0.22)                     (0.23)           
           Reassignment (#t+1#)            0.73***                    0.87***          
                                           (0.21)                     (0.24)           
           Reassignment (#t+2#)            0.71**                     0.97***          
                                           (0.23)                     (0.28)           
           R2                               0.96                       0.95            
           N                                4,944                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                                         
           Weights                           X                           X             
           Full sample                        X                                        
           Election FE                                                  X              
           Clean sample                                                                
           Balanced sample                                                             
          ------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.31                      -0.18           
                                           (0.25)                     (0.19)           
           Reassignment (#t-3#)             0.08                       -0.04           
                                           (0.22)                     (0.24)           
           Reassignment (#t-2#)             -0.15                      -0.23           
                                           (0.18)                     (0.18)           
           Reassignment (#t+0#)             0.66*                     1.32***          
                                           (0.29)                     (0.31)           
           Reassignment (#t+1#)            1.20***                    1.24***          
                                           (0.30)                     (0.32)           
           Reassignment (#t+2#)            1.27***                    1.28***          
                                           (0.35)                     (0.35)           
           R2                               0.97                       0.96            
           N                                2,040                      3,456           
           #treated precincts               150                         94             
           #control precincts               105                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                           X                          X              
           Full sample                                                                 
           Election FE                                                                 
           Clean sample                       X                                        
           Balanced sample                                               X             
          ------------------------------------------------------------------------------

(MWFE estimator converged in 7 iterations)
note: F7event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,082
Absorbing 2 HDFE groups                           F(  27,    391) =      15.84
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9622
                                                  Adj R-squared   =     0.9528
                                                  Within R-sq.    =     0.2401
Number of clusters (sb_new)  =        392         Root MSE        =     1.6562

                                    (Std. err. adjusted for 392 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |  -.6703861   .5172654    -1.30   0.196    -1.687356    .3465835
          F5event |  -.6583595   .4223022    -1.56   0.120    -1.488627    .1719076
          F4event |  -.4082101   .4496991    -0.91   0.365    -1.292341    .4759206
          F3event |  -.7596294   .5514171    -1.38   0.169    -1.843743    .3244841
          F2event |  -.2554424   .4314592    -0.59   0.554    -1.103713    .5928279
          L0event |   .6616041   .3893929     1.70   0.090    -.1039618     1.42717
          L1event |   1.353566   .3946713     3.43   0.001     .5776227     2.12951
          L2event |    1.20019   .5314938     2.26   0.024     .1552465    2.245133
          L3event |   1.102775   .6236048     1.77   0.078    -.1232631    2.328813
          L4event |   2.163033    .575693     3.76   0.000     1.031192    3.294874
          L5event |   2.510665   .7166434     3.50   0.001     1.101709    3.919622
          L6event |   1.055202   .7102535     1.49   0.138    -.3411917    2.451596
          L7event |  -1.637097   .8583984    -1.91   0.057    -3.324751    .0505569
        ln_ew_ges |   1.658953   1.549809     1.07   0.285    -1.388049    4.705955
         ew_biodt |   .4058558   .0345101    11.76   0.000     .3380072    .4737043
        ew_dtmihi |  -.2097621   .0717612    -2.92   0.004    -.3508482    -.068676
         ew_ledig |   .2638809    .088875     2.97   0.003     .0891482    .4386137
       ew_married |   .2916019   .0885021     3.29   0.001     .1176023    .4656014
        wb_anteil |  -.2650743   .0288145    -9.20   0.000     -.321725   -.2084236
          wb_ausl |  -.0830858   .0191134    -4.35   0.000    -.1206637    -.045508
         wb_18t24 |  -.0379134   .0324514    -1.17   0.243    -.1017144    .0258877
         wb_25t34 |   .0377339   .0228948     1.65   0.100    -.0072785    .0827462
         wb_35t44 |   .0018004   .0285127     0.06   0.950    -.0542569    .0578577
         wb_45t59 |  -.0582293   .0240926    -2.42   0.016    -.1055965   -.0108621
          avg_dur |   .0604399   .0283174     2.13   0.033     .0047664    .1161134
          hh_kids |   -.059445    .051796    -1.15   0.252    -.1612785    .0423886
mpreis_flats_rent |  -.0297394   .0309205    -0.96   0.337    -.0905306    .0310519
            _cons |  -10.62247   13.50364    -0.79   0.432    -37.17128    15.92635
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       392         392           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         54         54

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.24                      -0.23           
                                           (0.16)                     (0.16)           
           Reassignment (#t-3#)             0.02                       0.01            
                                           (0.15)                     (0.15)           
           Reassignment (#t-2#)             -0.07                      -0.06           
                                           (0.12)                     (0.12)           
           Reassignment (#t+0#)            0.66**                     0.61**           
                                           (0.22)                     (0.22)           
           Reassignment (#t+1#)            0.93***                    0.90***          
                                           (0.23)                     (0.23)           
           Reassignment (#t+2#)            1.07***                    1.05***          
                                           (0.26)                     (0.26)           
           R2                               0.96                       0.96            
           N                                4,666                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                                                       X             
           Full sample                                                                 
           Election FE                                                                 
           Clean sample                                                                
           Balanced sample                                                             
           No boundary change                                                          
          ------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.21                      -0.11           
                                           (0.16)                     (0.17)           
           Reassignment (#t-3#)             0.02                       -0.11           
                                           (0.15)                     (0.20)           
           Reassignment (#t-2#)             -0.05                      -0.17           
                                           (0.13)                     (0.14)           
           Reassignment (#t+0#)            0.62**                      0.54*           
                                           (0.22)                     (0.23)           
           Reassignment (#t+1#)            0.73***                    0.87***          
                                           (0.21)                     (0.24)           
           Reassignment (#t+2#)            0.71**                     0.97***          
                                           (0.23)                     (0.28)           
           R2                               0.96                       0.95            
           N                                4,944                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                                         
           Weights                           X                           X             
           Full sample                        X                                        
           Election FE                                                  X              
           Clean sample                                                                
           Balanced sample                                                             
           No boundary change                                                          
          ------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.31                      -0.18           
                                           (0.25)                     (0.19)           
           Reassignment (#t-3#)             0.08                       -0.04           
                                           (0.22)                     (0.24)           
           Reassignment (#t-2#)             -0.15                      -0.23           
                                           (0.18)                     (0.18)           
           Reassignment (#t+0#)             0.66*                     1.32***          
                                           (0.29)                     (0.31)           
           Reassignment (#t+1#)            1.20***                    1.24***          
                                           (0.30)                     (0.32)           
           Reassignment (#t+2#)            1.27***                    1.28***          
                                           (0.35)                     (0.35)           
           R2                               0.97                       0.96            
           N                                2,040                      3,456           
           #treated precincts               150                         94             
           #control precincts               105                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                           X                          X              
           Full sample                                                                 
           Election FE                                                                 
           Clean sample                       X                                        
           Balanced sample                                               X             
           No boundary change                                                          
          ------------------------------------------------------------------------------


                       ---------------------------------------------------
                                               Effect on mail-in turnout 
                       ---------------------------------------------------
                        Reassignment (#t-4#)             -0.41           
                                                        (0.45)           
                        Reassignment (#t-3#)             -0.76           
                                                        (0.55)           
                        Reassignment (#t-2#)             -0.26           
                                                        (0.43)           
                        Reassignment (#t+0#)             0.66            
                                                        (0.39)           
                        Reassignment (#t+1#)            1.35***          
                                                        (0.39)           
                        Reassignment (#t+2#)             1.20*           
                                                        (0.53)           
                        R2                               0.96            
                        N                                3,082           
                        #treated precincts                54             
                        #control precincts               338             
                        Precinct FE                       X              
                        Election-District FE              X              
                        Weights                           X              
                        Full sample                                      
                        Election FE                                      
                        Clean sample                                     
                        Balanced sample                                  
                        No boundary change                 X             
                       ---------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.24                      -0.23           
                                           (0.16)                     (0.16)           
           Reassignment (#t-3#)             0.02                       0.01            
                                           (0.15)                     (0.15)           
           Reassignment (#t-2#)             -0.07                      -0.06           
                                           (0.12)                     (0.12)           
           Reassignment (#t+0#)            0.66**                     0.61**           
                                           (0.22)                     (0.22)           
           Reassignment (#t+1#)            0.93***                    0.90***          
                                           (0.23)                     (0.23)           
           Reassignment (#t+2#)            1.07***                    1.05***          
                                           (0.26)                     (0.26)           
           R2                               0.96                       0.96            
           N                                4,666                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                                                       X             
           Full sample                                                                 
           Election FE                                                                 
           Clean sample                                                                
           Balanced sample                                                             
           No boundary change                                                          
          ------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.21                      -0.11           
                                           (0.16)                     (0.17)           
           Reassignment (#t-3#)             0.02                       -0.11           
                                           (0.15)                     (0.20)           
           Reassignment (#t-2#)             -0.05                      -0.17           
                                           (0.13)                     (0.14)           
           Reassignment (#t+0#)            0.62**                      0.54*           
                                           (0.22)                     (0.23)           
           Reassignment (#t+1#)            0.73***                    0.87***          
                                           (0.21)                     (0.24)           
           Reassignment (#t+2#)            0.71**                     0.97***          
                                           (0.23)                     (0.28)           
           R2                               0.96                       0.95            
           N                                4,944                      4,666           
           #treated precincts               280                        280             
           #control precincts               338                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                                         
           Weights                           X                           X             
           Full sample                        X                                        
           Election FE                                                  X              
           Clean sample                                                                
           Balanced sample                                                             
           No boundary change                                                          
          ------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Reassignment (#t-4#)             -0.31                      -0.18           
                                           (0.25)                     (0.19)           
           Reassignment (#t-3#)             0.08                       -0.04           
                                           (0.22)                     (0.24)           
           Reassignment (#t-2#)             -0.15                      -0.23           
                                           (0.18)                     (0.18)           
           Reassignment (#t+0#)             0.66*                     1.32***          
                                           (0.29)                     (0.31)           
           Reassignment (#t+1#)            1.20***                    1.24***          
                                           (0.30)                     (0.32)           
           Reassignment (#t+2#)            1.27***                    1.28***          
                                           (0.35)                     (0.35)           
           R2                               0.97                       0.96            
           N                                2,040                      3,456           
           #treated precincts               150                         94             
           #control precincts               105                        338             
           Precinct FE                       X                          X              
           Election-District FE              X                          X              
           Weights                           X                          X              
           Full sample                                                                 
           Election FE                                                                 
           Clean sample                       X                                        
           Balanced sample                                               X             
           No boundary change                                                          
          ------------------------------------------------------------------------------


                       ---------------------------------------------------
                                               Effect on mail-in turnout 
                       ---------------------------------------------------
                        Reassignment (#t-4#)             -0.41           
                                                        (0.45)           
                        Reassignment (#t-3#)             -0.76           
                                                        (0.55)           
                        Reassignment (#t-2#)             -0.26           
                                                        (0.43)           
                        Reassignment (#t+0#)             0.66            
                                                        (0.39)           
                        Reassignment (#t+1#)            1.35***          
                                                        (0.39)           
                        Reassignment (#t+2#)             1.20*           
                                                        (0.53)           
                        R2                               0.96            
                        N                                3,082           
                        #treated precincts                54             
                        #control precincts               338             
                        Precinct FE                       X              
                        Election-District FE              X              
                        Weights                           X              
                        Full sample                                      
                        Election FE                                      
                        Clean sample                                     
                        Balanced sample                                  
                        No boundary change                 X             
                       ---------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9900
                                                  Adj R-squared   =     0.9878
                                                  Within R-sq.    =     0.4228
Number of clusters (sb_new)  =        618         Root MSE        =     1.6217

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0839285   .3296682    -0.25   0.799    -.7313362    .5634791
          F6event |   .2369716   .2816898     0.84   0.401    -.3162155    .7901586
          F5event |  -.2444267   .2617171    -0.93   0.351     -.758391    .2695377
          F4event |  -.2124752   .1680335    -1.26   0.207    -.5424621    .1175118
          F3event |  -.0506398   .1561578    -0.32   0.746    -.3573051    .2560254
          F2event |  -.0629027   .1311889    -0.48   0.632    -.3205335    .1947281
          L0event |  -.3617874   .1654194    -2.19   0.029    -.6866407   -.0369342
          L1event |   .0437473   .2037249     0.21   0.830    -.3563311    .4438256
          L2event |   .3084572   .2264188     1.36   0.174    -.1361877     .753102
          L3event |   .0917893   .2384579     0.38   0.700    -.3764983    .5600769
          L4event |   .6689228   .6313905     1.06   0.290     -.571012    1.908858
          L5event |   1.871175   .7330755     2.55   0.011     .4315488      3.3108
          L6event |   .6988491   .8229426     0.85   0.396    -.9172591    2.314957
          L7event |   .5276146   1.038361     0.51   0.612    -1.511535    2.566765
        ln_ew_ges |   1.924952   1.128495     1.71   0.089    -.2912054     4.14111
         ew_biodt |   .7508359   .0320015    23.46   0.000     .6879909    .8136809
        ew_dtmihi |  -.1773487   .0528889    -3.35   0.001    -.2812128   -.0734847
         ew_ledig |   .4309478   .0705828     6.11   0.000     .2923361    .5695595
       ew_married |   .6435649   .0691374     9.31   0.000     .5077917     .779338
        wb_anteil |  -.5187913   .0248334   -20.89   0.000    -.5675595   -.4700231
          wb_ausl |  -.0529695   .0172491    -3.07   0.002    -.0868436   -.0190953
         wb_18t24 |  -.0496937   .0252917    -1.96   0.050     -.099362   -.0000254
         wb_25t34 |  -.0184208   .0165939    -1.11   0.267    -.0510083    .0141666
         wb_35t44 |  -.0050013   .0207031    -0.24   0.809    -.0456583    .0356558
         wb_45t59 |  -.0242679   .0195815    -1.24   0.216    -.0627224    .0141867
          avg_dur |   .0164797   .0225464     0.73   0.465    -.0277972    .0607566
          hh_kids |  -.1072025   .0367355    -2.92   0.004    -.1793444   -.0350607
mpreis_flats_rent |   .0158927   .0237703     0.67   0.504    -.0307879    .0625732
            _cons |  -2.538923   10.46686    -0.24   0.808    -23.09391    18.01607
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
warning: matrix in addrows option has varying size rows:
   `"R2 "',`"0.99"' \ `"N"',`"4,666"'\#treated precincts, 280 \ #control precincts, 338 \ Precinct
>  FE, X \ Election-District FE, X \ Weights, 
warning: no existing table found for merge or append

                        -------------------------------------------------
                                                Effect on total turnout 
                        -------------------------------------------------
                         Reassignment (#t-4#)            -0.21          
                                                        (0.17)          
                         Reassignment (#t-3#)            -0.05          
                                                        (0.16)          
                         Reassignment (#t-2#)            -0.06          
                                                        (0.13)          
                         Reassignment (#t+0#)           -0.36*          
                                                        (0.17)          
                         Reassignment (#t+1#)            0.04           
                                                        (0.20)          
                         Reassignment (#t+2#)            0.31           
                                                        (0.23)          
                         R2                              0.99           
                         N                               4,666          
                         #treated precincts              280            
                         #control precincts              338            
                         Precinct FE                      X             
                         Election-District FE             X             
                         Weights                                        
                        -------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4395
Number of clusters (sb_new)  =        618         Root MSE        =     1.6245

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3259399    -0.25   0.800    -.7224958    .5576764
          F6event |   .2549675   .2761707     0.92   0.356    -.2873811    .7973161
          F5event |  -.2369689   .2619342    -0.90   0.366    -.7513594    .2774217
          F4event |  -.2192898   .1694872    -1.29   0.196    -.5521315     .113552
          F3event |  -.0490231   .1579758    -0.31   0.756    -.3592586    .2612124
          F2event |  -.0537065   .1322374    -0.41   0.685    -.3133964    .2059834
          L0event |  -.3892706   .1633916    -2.38   0.018    -.7101417   -.0683996
          L1event |    .011072    .200952     0.06   0.956    -.3835608    .4057048
          L2event |   .2959665    .224252     1.32   0.187    -.1444233    .7363563
          L3event |   .1243464   .2332215     0.53   0.594    -.3336578    .5823505
          L4event |   .6528057   .6207241     1.05   0.293    -.5661824    1.871794
          L5event |   1.807978   .7132727     2.53   0.011      .407241    3.208714
          L6event |   .6923315   .7952045     0.87   0.384     -.869304    2.253967
          L7event |   .4397263   1.099927     0.40   0.689    -1.720328    2.599781
        ln_ew_ges |   1.678109   1.068642     1.57   0.117    -.4205078    3.776725
         ew_biodt |    .761748   .0314252    24.24   0.000     .7000348    .8234612
        ew_dtmihi |  -.1673742   .0522282    -3.20   0.001    -.2699407   -.0648076
         ew_ledig |   .4238555   .0699086     6.06   0.000     .2865679    .5611432
       ew_married |   .6387186   .0688582     9.28   0.000     .5034938    .7739433
        wb_anteil |  -.5320422   .0239517   -22.21   0.000     -.579079   -.4850055
          wb_ausl |  -.0535363   .0175921    -3.04   0.002     -.088084   -.0189885
         wb_18t24 |  -.0438039    .025903    -1.69   0.091    -.0946727    .0070648
         wb_25t34 |  -.0197863   .0166674    -1.19   0.236     -.052518    .0129454
         wb_35t44 |  -.0028286   .0208991    -0.14   0.892    -.0438706    .0382134
         wb_45t59 |  -.0241236   .0196706    -1.23   0.221    -.0627531    .0145059
          avg_dur |   .0170995    .022054     0.78   0.438    -.0262106    .0604096
          hh_kids |  -.1148866    .035755    -3.21   0.001    -.1851028   -.0446704
mpreis_flats_rent |   .0148646   .0236111     0.63   0.529    -.0315033    .0612326
            _cons |   -.366715   10.03193    -0.04   0.971    -20.06759    19.33416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.05                    -0.05          
                                            (0.16)                   (0.16)          
             Reassignment (#t-2#)            -0.06                    -0.05          
                                            (0.13)                   (0.13)          
             Reassignment (#t+0#)           -0.36*                   -0.39*          
                                            (0.17)                   (0.16)          
             Reassignment (#t+1#)            0.04                     0.01           
                                            (0.20)                   (0.20)          
             Reassignment (#t+2#)            0.31                     0.30           
                                            (0.23)                   (0.22)          
             R2                              0.99                     0.99           
             N                               4,666                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                                                    X            
            --------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  28,    617) =      50.24
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9898
                                                  Adj R-squared   =     0.9877
                                                  Within R-sq.    =     0.4472
Number of clusters (sb_new)  =        618         Root MSE        =     1.6455

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0665807   .3236588    -0.21   0.837    -.7021872    .5690257
          F6event |   .2637803   .2749529     0.96   0.338    -.2761766    .8037373
          F5event |  -.2082253   .2641006    -0.79   0.431    -.7268703    .3104197
          F4event |  -.2109017   .1668924    -1.26   0.207    -.5386478    .1168444
          F3event |  -.0449621   .1584785    -0.28   0.777    -.3561847    .2662605
          F2event |  -.0381553   .1319306    -0.29   0.773    -.2972429    .2209322
          L0event |  -.4034044   .1625033    -2.48   0.013    -.7225311   -.0842777
          L1event |  -.0766197   .1944822    -0.39   0.694    -.4585469    .3053075
          L2event |   .1813603   .2047452     0.89   0.376    -.2207216    .5834422
          L3event |   .1705327   .2214758     0.77   0.442     -.264405    .6054705
          L4event |   .8868331   .5416747     1.64   0.102    -.1769165    1.950583
          L5event |   1.558868   .5180841     3.01   0.003     .5414455     2.57629
          L6event |   1.337228   .6557043     2.04   0.042     .0495451    2.624911
          L7event |    1.40591   .7650262     1.84   0.067    -.0964613     2.90828
        ln_ew_ges |   1.709846   .9805698     1.74   0.082    -.2158131    3.635505
         ew_biodt |   .7741969   .0304774    25.40   0.000     .7143448     .834049
        ew_dtmihi |  -.1510625   .0504027    -3.00   0.003    -.2500442   -.0520807
         ew_ledig |   .4244762   .0615634     6.89   0.000     .3035769    .5453755
       ew_married |   .6593486   .0601727    10.96   0.000     .5411805    .7775166
        wb_anteil |  -.5415808   .0237296   -22.82   0.000    -.5881813   -.4949803
          wb_ausl |  -.0592567    .017693    -3.35   0.001    -.0940025    -.024511
         wb_18t24 |  -.0362178   .0252801    -1.43   0.152    -.0858633    .0134276
         wb_25t34 |  -.0164161   .0167363    -0.98   0.327    -.0492832    .0164509
         wb_35t44 |  -.0054146   .0201549    -0.27   0.788    -.0449951     .034166
         wb_45t59 |  -.0313649    .019311    -1.62   0.105    -.0692882    .0065585
          avg_dur |   .0168578   .0213148     0.79   0.429    -.0250006    .0587162
          hh_kids |  -.1266422   .0334325    -3.79   0.000    -.1922975   -.0609869
mpreis_flats_rent |    .024086   .0222442     1.08   0.279    -.0195974    .0677695
            _cons |  -1.459861    9.06066    -0.16   0.872    -19.25333    16.33361
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.05                    -0.05          
                                            (0.16)                   (0.16)          
             Reassignment (#t-2#)            -0.06                    -0.05          
                                            (0.13)                   (0.13)          
             Reassignment (#t+0#)           -0.36*                   -0.39*          
                                            (0.17)                   (0.16)          
             Reassignment (#t+1#)            0.04                     0.01           
                                            (0.20)                   (0.20)          
             Reassignment (#t+2#)            0.31                     0.30           
                                            (0.23)                   (0.22)          
             R2                              0.99                     0.99           
             N                               4,666                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                                                    X            
             Full sample                                                             
            --------------------------------------------------------------------------


                        -------------------------------------------------
                                                Effect on total turnout 
                        -------------------------------------------------
                         Reassignment (#t-4#)            -0.21          
                                                        (0.17)          
                         Reassignment (#t-3#)            -0.04          
                                                        (0.16)          
                         Reassignment (#t-2#)            -0.04          
                                                        (0.13)          
                         Reassignment (#t+0#)           -0.40*          
                                                        (0.16)          
                         Reassignment (#t+1#)            -0.08          
                                                        (0.19)          
                         Reassignment (#t+2#)            0.18           
                                                        (0.20)          
                         R2                              0.99           
                         N                               4,944          
                         #treated precincts              280            
                         #control precincts              338            
                         Precinct FE                      X             
                         Election-District FE             X             
                         Weights                          X             
                         Full sample                       X            
                        -------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      45.00
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9875
                                                  Adj R-squared   =     0.9855
                                                  Within R-sq.    =     0.4457
Number of clusters (sb_new)  =        618         Root MSE        =     1.7997

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0049977   .3288787     0.02   0.988    -.6408596     .650855
          F6event |   .1547925   .2763992     0.56   0.576    -.3880047    .6975897
          F5event |  -.2292509   .2737864    -0.84   0.403     -.766917    .3084152
          F4event |  -.2241174   .1715177    -1.31   0.192    -.5609465    .1127118
          F3event |  -.1519224   .1677705    -0.91   0.366    -.4813929    .1775481
          F2event |  -.0112714   .1532364    -0.07   0.941    -.3121996    .2896568
          L0event |  -.5300509    .167397    -3.17   0.002    -.8587878   -.2013139
          L1event |  -.0018816   .2026481    -0.01   0.993    -.3998451     .396082
          L2event |   .2664154   .2414528     1.10   0.270    -.2077536    .7405844
          L3event |  -.0110278   .2511612    -0.04   0.965    -.5042622    .4822066
          L4event |   1.429649   .7515148     1.90   0.058    -.0461877    2.905486
          L5event |   1.477225   .6837042     2.16   0.031     .1345559    2.819895
          L6event |   .4332568   .9361461     0.46   0.644    -1.405162    2.271676
          L7event |   .5087222   .8213052     0.62   0.536     -1.10417    2.121615
        ln_ew_ges |   1.650218   1.072406     1.54   0.124    -.4557902    3.756225
         ew_biodt |   .7902138   .0329271    24.00   0.000      .725551    .8548767
        ew_dtmihi |  -.1665338   .0542449    -3.07   0.002    -.2730608   -.0600067
         ew_ledig |   .3830132   .0745097     5.14   0.000     .2366899    .5293365
       ew_married |   .6156742   .0733212     8.40   0.000     .4716849    .7596636
        wb_anteil |    -.54133   .0277962   -19.47   0.000    -.5959166   -.4867435
          wb_ausl |  -.0348043   .0125578    -2.77   0.006    -.0594655   -.0101431
         wb_18t24 |   -.056542   .0267672    -2.11   0.035    -.1091079    -.003976
         wb_25t34 |  -.0117404   .0144449    -0.81   0.417    -.0401076    .0166268
         wb_35t44 |  -.0065333   .0192077    -0.34   0.734    -.0442536     .031187
         wb_45t59 |   -.012029   .0192847    -0.62   0.533    -.0499007    .0258427
          avg_dur |   .0416426   .0224601     1.85   0.064    -.0024648      .08575
          hh_kids |  -.0901732   .0367571    -2.45   0.014    -.1623574    -.017989
mpreis_flats_rent |   .1036902    .020883     4.97   0.000     .0626798    .1447006
            _cons |  -1.293706   10.38581    -0.12   0.901    -21.68953    19.10211
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        280        280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.05                    -0.05          
                                            (0.16)                   (0.16)          
             Reassignment (#t-2#)            -0.06                    -0.05          
                                            (0.13)                   (0.13)          
             Reassignment (#t+0#)           -0.36*                   -0.39*          
                                            (0.17)                   (0.16)          
             Reassignment (#t+1#)            0.04                     0.01           
                                            (0.20)                   (0.20)          
             Reassignment (#t+2#)            0.31                     0.30           
                                            (0.23)                   (0.22)          
             R2                              0.99                     0.99           
             N                               4,666                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                                                    X            
             Full sample                                                             
             Election FE                                                             
            --------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.04                    -0.15          
                                            (0.16)                   (0.17)          
             Reassignment (#t-2#)            -0.04                    -0.01          
                                            (0.13)                   (0.15)          
             Reassignment (#t+0#)           -0.40*                   -0.53**         
                                            (0.16)                   (0.17)          
             Reassignment (#t+1#)            -0.08                    -0.00          
                                            (0.19)                   (0.20)          
             Reassignment (#t+2#)            0.18                     0.27           
                                            (0.20)                   (0.24)          
             R2                              0.99                     0.99           
             N                               4,944                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                                      
             Weights                          X                         X            
             Full sample                       X                                     
             Election FE                                               X             
            --------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  28,    254) =      23.25
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9874
                                                  Within R-sq.    =     0.4324
Number of clusters (sb_new)  =        255         Root MSE        =     1.6321

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.3792845   .4414604    -0.86   0.391    -1.248673    .4901045
          F6event |   .1300258   .3802424     0.34   0.733    -.6188036    .8788552
          F5event |  -.4988274    .410548    -1.22   0.225    -1.307339    .3096844
          F4event |  -.2910344   .2541336    -1.15   0.253    -.7915118     .209443
          F3event |  -.1213751   .2349149    -0.52   0.606    -.5840042     .341254
          F2event |  -.1415156   .2128105    -0.66   0.507    -.5606135    .2775823
          L0event |  -.6448859   .2562473    -2.52   0.012    -1.149526    -.140246
          L1event |  -.2848778   .2905263    -0.98   0.328     -.857025    .2872694
          L2event |   .1289917   .2965282     0.44   0.664    -.4549754    .7129589
          L3event |    .094101   .3485878     0.27   0.787    -.5923894    .7805915
          L4event |  -.2140303   .9041641    -0.24   0.813    -1.994644    1.566583
          L5event |   1.218689   .7778781     1.57   0.118    -.3132233    2.750601
          L6event |   .3430585    .993904     0.35   0.730    -1.614284    2.300401
          L7event |   .5568683   1.048685     0.53   0.596    -1.508357    2.622093
        ln_ew_ges |   1.506273   1.937219     0.78   0.438    -2.308785    5.321331
         ew_biodt |   .7256597     .05209    13.93   0.000     .6230765     .828243
        ew_dtmihi |  -.1123104   .0822001    -1.37   0.173    -.2741908    .0495701
         ew_ledig |   .4666324   .0928875     5.02   0.000     .2837045    .6495602
       ew_married |   .7544649   .0973565     7.75   0.000     .5627362    .9461937
        wb_anteil |  -.5177767    .035174   -14.72   0.000    -.5870466   -.4485067
          wb_ausl |  -.0558392   .0231391    -2.41   0.017    -.1014081   -.0102702
         wb_18t24 |  -.0529646    .037597    -1.41   0.160    -.1270061    .0210769
         wb_25t34 |  -.0443931   .0282842    -1.57   0.118    -.1000945    .0113083
         wb_35t44 |  -.0267751   .0293222    -0.91   0.362    -.0845207    .0309705
         wb_45t59 |   -.020394   .0287425    -0.71   0.479    -.0769979    .0362099
          avg_dur |  -.0110793   .0377724    -0.29   0.770    -.0854662    .0633076
          hh_kids |  -.1246045   .0566063    -2.20   0.029     -.236082   -.0131271
mpreis_flats_rent |  -.0099424    .038141    -0.26   0.795    -.0850554    .0651705
            _cons |  -2.413006   16.75033    -0.14   0.886    -35.40023    30.57422
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        840        105

            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.05                    -0.05          
                                            (0.16)                   (0.16)          
             Reassignment (#t-2#)            -0.06                    -0.05          
                                            (0.13)                   (0.13)          
             Reassignment (#t+0#)           -0.36*                   -0.39*          
                                            (0.17)                   (0.16)          
             Reassignment (#t+1#)            0.04                     0.01           
                                            (0.20)                   (0.20)          
             Reassignment (#t+2#)            0.31                     0.30           
                                            (0.23)                   (0.22)          
             R2                              0.99                     0.99           
             N                               4,666                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                                                    X            
             Full sample                                                             
             Election FE                                                             
             Clean sample                                                            
            --------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.04                    -0.15          
                                            (0.16)                   (0.17)          
             Reassignment (#t-2#)            -0.04                    -0.01          
                                            (0.13)                   (0.15)          
             Reassignment (#t+0#)           -0.40*                   -0.53**         
                                            (0.16)                   (0.17)          
             Reassignment (#t+1#)            -0.08                    -0.00          
                                            (0.19)                   (0.20)          
             Reassignment (#t+2#)            0.18                     0.27           
                                            (0.20)                   (0.24)          
             R2                              0.99                     0.99           
             N                               4,944                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                                      
             Weights                          X                         X            
             Full sample                       X                                     
             Election FE                                               X             
             Clean sample                                                            
            --------------------------------------------------------------------------


                        -------------------------------------------------
                                                Effect on total turnout 
                        -------------------------------------------------
                         Reassignment (#t-4#)            -0.29          
                                                        (0.25)          
                         Reassignment (#t-3#)            -0.12          
                                                        (0.23)          
                         Reassignment (#t-2#)            -0.14          
                                                        (0.21)          
                         Reassignment (#t+0#)           -0.64*          
                                                        (0.26)          
                         Reassignment (#t+1#)            -0.28          
                                                        (0.29)          
                         Reassignment (#t+2#)            0.13           
                                                        (0.30)          
                         R2                              0.99           
                         N                               2,040          
                         #treated precincts              150            
                         #control precincts              105            
                         Precinct FE                      X             
                         Election-District FE             X             
                         Weights                          X             
                         Full sample                                    
                         Election FE                                    
                         Clean sample                      X            
                        -------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  22,    431) =      53.71
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9902
                                                  Adj R-squared   =     0.9879
                                                  Within R-sq.    =     0.4568
Number of clusters (sb_new)  =        432         Root MSE        =     1.6375

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |   .3844237   .4442074     0.87   0.387    -.4886585    1.257506
          F4event |  -.1478234    .246982    -0.60   0.550    -.6332624    .3376156
          F3event |   -.027034   .2683297    -0.10   0.920    -.5544316    .5003636
          F2event |  -.0404714   .2418723    -0.17   0.867    -.5158674    .4349246
          L0event |  -.2518697   .2797461    -0.90   0.368    -.8017059    .2979666
          L1event |  -.1637918   .3079028    -0.53   0.595    -.7689696     .441386
          L2event |    .471916    .286319     1.65   0.100    -.0908392    1.034671
          L3event |   .0519235   .3061151     0.17   0.865    -.5497407    .6535878
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
        ln_ew_ges |   1.149485    1.06457     1.08   0.281    -.9429108     3.24188
         ew_biodt |   .7780234   .0344847    22.56   0.000     .7102443    .8458025
        ew_dtmihi |   -.155998   .0569907    -2.74   0.006    -.2680123   -.0439838
         ew_ledig |    .490452   .0720319     6.81   0.000     .3488745    .6320295
       ew_married |   .6906151   .0716153     9.64   0.000     .5498564    .8313738
        wb_anteil |  -.5332059   .0259817   -20.52   0.000    -.5842725   -.4821392
          wb_ausl |  -.0408679   .0213478    -1.91   0.056    -.0828267     .001091
         wb_18t24 |   -.045997   .0298981    -1.54   0.125    -.1047612    .0127672
         wb_25t34 |  -.0166158   .0200613    -0.83   0.408     -.056046    .0228143
         wb_35t44 |  -.0216666   .0243587    -0.89   0.374    -.0695433      .02621
         wb_45t59 |  -.0198458   .0220381    -0.90   0.368    -.0631614    .0234697
          avg_dur |   .0032788    .025126     0.13   0.896    -.0461059    .0526635
          hh_kids |  -.0972835   .0423164    -2.30   0.022    -.1804558   -.0141113
mpreis_flats_rent |    .024928   .0278387     0.90   0.371    -.0297886    .0796445
            _cons |   -2.81942   10.50688    -0.27   0.789    -23.47053    17.83168
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.05                    -0.05          
                                            (0.16)                   (0.16)          
             Reassignment (#t-2#)            -0.06                    -0.05          
                                            (0.13)                   (0.13)          
             Reassignment (#t+0#)           -0.36*                   -0.39*          
                                            (0.17)                   (0.16)          
             Reassignment (#t+1#)            0.04                     0.01           
                                            (0.20)                   (0.20)          
             Reassignment (#t+2#)            0.31                     0.30           
                                            (0.23)                   (0.22)          
             R2                              0.99                     0.99           
             N                               4,666                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                                                    X            
             Full sample                                                             
             Election FE                                                             
             Clean sample                                                            
             Balanced sample                                                         
            --------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.04                    -0.15          
                                            (0.16)                   (0.17)          
             Reassignment (#t-2#)            -0.04                    -0.01          
                                            (0.13)                   (0.15)          
             Reassignment (#t+0#)           -0.40*                   -0.53**         
                                            (0.16)                   (0.17)          
             Reassignment (#t+1#)            -0.08                    -0.00          
                                            (0.19)                   (0.20)          
             Reassignment (#t+2#)            0.18                     0.27           
                                            (0.20)                   (0.24)          
             R2                              0.99                     0.99           
             N                               4,944                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                                      
             Weights                          X                         X            
             Full sample                       X                                     
             Election FE                                               X             
             Clean sample                                                            
             Balanced sample                                                         
            --------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.29                    -0.15          
                                            (0.25)                   (0.25)          
             Reassignment (#t-3#)            -0.12                    -0.03          
                                            (0.23)                   (0.27)          
             Reassignment (#t-2#)            -0.14                    -0.04          
                                            (0.21)                   (0.24)          
             Reassignment (#t+0#)           -0.64*                    -0.25          
                                            (0.26)                   (0.28)          
             Reassignment (#t+1#)            -0.28                    -0.16          
                                            (0.29)                   (0.31)          
             Reassignment (#t+2#)            0.13                     0.47           
                                            (0.30)                   (0.29)          
             R2                              0.99                     0.99           
             N                               2,040                    3,456          
             #treated precincts              150                       94            
             #control precincts              105                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                          X                        X             
             Full sample                                                             
             Election FE                                                             
             Clean sample                      X                                     
             Balanced sample                                            X            
            --------------------------------------------------------------------------

(MWFE estimator converged in 7 iterations)
note: F7event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,082
Absorbing 2 HDFE groups                           F(  27,    391) =      34.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9898
                                                  Adj R-squared   =     0.9873
                                                  Within R-sq.    =     0.4495
Number of clusters (sb_new)  =        392         Root MSE        =     1.6793

                                    (Std. err. adjusted for 392 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |  -.0673472   .4827936    -0.14   0.889    -1.016543    .8818489
          F5event |   .0365818   .4035009     0.09   0.928     -.756721    .8298845
          F4event |    .091248   .5044637     0.18   0.857    -.9005527    1.083049
          F3event |  -.1346075   .4810902    -0.28   0.780    -1.080455    .8112396
          F2event |   .2277614   .3501037     0.65   0.516      -.46056    .9160827
          L0event |  -.7036765   .3033067    -2.32   0.021    -1.299993   -.1073603
          L1event |    .269528   .3855843     0.70   0.485      -.48855    1.027606
          L2event |   .3118873   .4353377     0.72   0.474    -.5440083    1.167783
          L3event |   .9677479   .4247156     2.28   0.023     .1327359     1.80276
          L4event |   1.790252   .5683063     3.15   0.002     .6729331     2.90757
          L5event |   2.133125   .9466598     2.25   0.025     .2719443    3.994305
          L6event |   1.207543   .7684729     1.57   0.117    -.3033132    2.718399
          L7event |   2.593908   .7016119     3.70   0.000     1.214505    3.973312
        ln_ew_ges |   .9016356     1.1828     0.76   0.446    -1.423807    3.227078
         ew_biodt |   .7971343   .0394061    20.23   0.000       .71966    .8746086
        ew_dtmihi |  -.1288826   .0636937    -2.02   0.044    -.2541076   -.0036577
         ew_ledig |   .4819775   .0807804     5.97   0.000     .3231591    .6407958
       ew_married |   .6642425    .080462     8.26   0.000     .5060501    .8224348
        wb_anteil |   -.535791   .0315147   -17.00   0.000    -.5977505   -.4738315
          wb_ausl |  -.0550399    .024456    -2.25   0.025    -.1031218   -.0069581
         wb_18t24 |  -.0429794   .0311077    -1.38   0.168    -.1041387    .0181798
         wb_25t34 |  -.0125336   .0204844    -0.61   0.541     -.052807    .0277397
         wb_35t44 |  -.0256853   .0257907    -1.00   0.320    -.0763911    .0250205
         wb_45t59 |  -.0207136   .0251271    -0.82   0.410    -.0701147    .0286876
          avg_dur |   .0224262   .0266192     0.84   0.400    -.0299086    .0747609
          hh_kids |  -.0761079   .0421866    -1.80   0.072    -.1590488    .0068329
mpreis_flats_rent |    .022885   .0330094     0.69   0.489    -.0420131    .0877831
            _cons |   -1.52422    12.1523    -0.13   0.900    -25.41625    22.36781
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       392         392           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         54         54

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.05                    -0.05          
                                            (0.16)                   (0.16)          
             Reassignment (#t-2#)            -0.06                    -0.05          
                                            (0.13)                   (0.13)          
             Reassignment (#t+0#)           -0.36*                   -0.39*          
                                            (0.17)                   (0.16)          
             Reassignment (#t+1#)            0.04                     0.01           
                                            (0.20)                   (0.20)          
             Reassignment (#t+2#)            0.31                     0.30           
                                            (0.23)                   (0.22)          
             R2                              0.99                     0.99           
             N                               4,666                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                                                    X            
             Full sample                                                             
             Election FE                                                             
             Clean sample                                                            
             Balanced sample                                                         
             No boundary change                                                      
            --------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.04                    -0.15          
                                            (0.16)                   (0.17)          
             Reassignment (#t-2#)            -0.04                    -0.01          
                                            (0.13)                   (0.15)          
             Reassignment (#t+0#)           -0.40*                   -0.53**         
                                            (0.16)                   (0.17)          
             Reassignment (#t+1#)            -0.08                    -0.00          
                                            (0.19)                   (0.20)          
             Reassignment (#t+2#)            0.18                     0.27           
                                            (0.20)                   (0.24)          
             R2                              0.99                     0.99           
             N                               4,944                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                                      
             Weights                          X                         X            
             Full sample                       X                                     
             Election FE                                               X             
             Clean sample                                                            
             Balanced sample                                                         
             No boundary change                                                      
            --------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.29                    -0.15          
                                            (0.25)                   (0.25)          
             Reassignment (#t-3#)            -0.12                    -0.03          
                                            (0.23)                   (0.27)          
             Reassignment (#t-2#)            -0.14                    -0.04          
                                            (0.21)                   (0.24)          
             Reassignment (#t+0#)           -0.64*                    -0.25          
                                            (0.26)                   (0.28)          
             Reassignment (#t+1#)            -0.28                    -0.16          
                                            (0.29)                   (0.31)          
             Reassignment (#t+2#)            0.13                     0.47           
                                            (0.30)                   (0.29)          
             R2                              0.99                     0.99           
             N                               2,040                    3,456          
             #treated precincts              150                       94            
             #control precincts              105                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                          X                        X             
             Full sample                                                             
             Election FE                                                             
             Clean sample                      X                                     
             Balanced sample                                            X            
             No boundary change                                                      
            --------------------------------------------------------------------------


                        -------------------------------------------------
                                                Effect on total turnout 
                        -------------------------------------------------
                         Reassignment (#t-4#)            0.09           
                                                        (0.50)          
                         Reassignment (#t-3#)            -0.13          
                                                        (0.48)          
                         Reassignment (#t-2#)            0.23           
                                                        (0.35)          
                         Reassignment (#t+0#)           -0.70*          
                                                        (0.30)          
                         Reassignment (#t+1#)            0.27           
                                                        (0.39)          
                         Reassignment (#t+2#)            0.31           
                                                        (0.44)          
                         R2                              0.99           
                         N                               3,082          
                         #treated precincts               54            
                         #control precincts              338            
                         Precinct FE                      X             
                         Election-District FE             X             
                         Weights                          X             
                         Full sample                                    
                         Election FE                                    
                         Clean sample                                   
                         Balanced sample                                
                         No boundary change                X            
                        -------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.05                    -0.05          
                                            (0.16)                   (0.16)          
             Reassignment (#t-2#)            -0.06                    -0.05          
                                            (0.13)                   (0.13)          
             Reassignment (#t+0#)           -0.36*                   -0.39*          
                                            (0.17)                   (0.16)          
             Reassignment (#t+1#)            0.04                     0.01           
                                            (0.20)                   (0.20)          
             Reassignment (#t+2#)            0.31                     0.30           
                                            (0.23)                   (0.22)          
             R2                              0.99                     0.99           
             N                               4,666                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                                                    X            
             Full sample                                                             
             Election FE                                                             
             Clean sample                                                            
             Balanced sample                                                         
             No boundary change                                                      
            --------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.21                    -0.22          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.04                    -0.15          
                                            (0.16)                   (0.17)          
             Reassignment (#t-2#)            -0.04                    -0.01          
                                            (0.13)                   (0.15)          
             Reassignment (#t+0#)           -0.40*                   -0.53**         
                                            (0.16)                   (0.17)          
             Reassignment (#t+1#)            -0.08                    -0.00          
                                            (0.19)                   (0.20)          
             Reassignment (#t+2#)            0.18                     0.27           
                                            (0.20)                   (0.24)          
             R2                              0.99                     0.99           
             N                               4,944                    4,666          
             #treated precincts              280                      280            
             #control precincts              338                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                                      
             Weights                          X                         X            
             Full sample                       X                                     
             Election FE                                               X             
             Clean sample                                                            
             Balanced sample                                                         
             No boundary change                                                      
            --------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Reassignment (#t-4#)            -0.29                    -0.15          
                                            (0.25)                   (0.25)          
             Reassignment (#t-3#)            -0.12                    -0.03          
                                            (0.23)                   (0.27)          
             Reassignment (#t-2#)            -0.14                    -0.04          
                                            (0.21)                   (0.24)          
             Reassignment (#t+0#)           -0.64*                    -0.25          
                                            (0.26)                   (0.28)          
             Reassignment (#t+1#)            -0.28                    -0.16          
                                            (0.29)                   (0.31)          
             Reassignment (#t+2#)            0.13                     0.47           
                                            (0.30)                   (0.29)          
             R2                              0.99                     0.99           
             N                               2,040                    3,456          
             #treated precincts              150                       94            
             #control precincts              105                      338            
             Precinct FE                      X                        X             
             Election-District FE             X                        X             
             Weights                          X                        X             
             Full sample                                                             
             Election FE                                                             
             Clean sample                      X                                     
             Balanced sample                                            X            
             No boundary change                                                      
            --------------------------------------------------------------------------


                        -------------------------------------------------
                                                Effect on total turnout 
                        -------------------------------------------------
                         Reassignment (#t-4#)            0.09           
                                                        (0.50)          
                         Reassignment (#t-3#)            -0.13          
                                                        (0.48)          
                         Reassignment (#t-2#)            0.23           
                                                        (0.35)          
                         Reassignment (#t+0#)           -0.70*          
                                                        (0.30)          
                         Reassignment (#t+1#)            0.27           
                                                        (0.39)          
                         Reassignment (#t+2#)            0.31           
                                                        (0.44)          
                         R2                              0.99           
                         N                               3,082          
                         #treated precincts               54            
                         #control precincts              338            
                         Precinct FE                      X             
                         Election-District FE             X             
                         Weights                          X             
                         Full sample                                    
                         Election FE                                    
                         Clean sample                                   
                         Balanced sample                                
                         No boundary change                X            
                        -------------------------------------------------


.                  outreg, replay(turnout_urne) ctitle("", (1), (2), (3), (4), (5), (6), (7) \ "\m
> idrule" \ "\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}" \"\midr
> ule") store(tab1) ///
>                                 addrow("\midrule" \ "\multicolumn{3}{l}{\textbf{Panel B:} Effect
>  on Turnout via Mail}" \ "\midrule") 
warning: matrix in ctitles option has varying size rows:
   "", (1), (2), (3), (4), (5), (6), (7) \ "\midrule" \ "\multicolumn{3}{l}{\textbf{Panel A:} Effe
> ct on Turnout at the Polling Place}" \"\midrule"

--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
                                                                                 (1)       (2)    
>    (3)       (4)       (5)       (6)      (7)    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                  
\midrule                                                                                          
>                                                  
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
Reassignment (#t-4#)                                                             0.03      0.01   
>    0.00     -0.12      0.02      0.04     0.50   
                                                                                (0.17)    (0.17)  
>   (0.18)    (0.20)    (0.24)    (0.25)   (0.50)  
Reassignment (#t-3#)                                                            -0.07     -0.06   
>   -0.06     -0.04     -0.20      0.02     0.63   
                                                                                (0.17)    (0.17)  
>   (0.17)    (0.21)    (0.25)    (0.26)   (0.52)  
Reassignment (#t-2#)                                                             0.01      0.01   
>    0.01      0.15      0.00      0.19     0.48   
                                                                                (0.12)    (0.12)  
>   (0.12)    (0.14)    (0.20)    (0.21)   (0.42)  
Reassignment (#t+0#)                                                           -1.02***  -1.00*** 
>  -1.02***  -1.07***  -1.31***  -1.57***  -1.37** 
                                                                                (0.23)    (0.23)  
>   (0.23)    (0.24)    (0.33)    (0.37)   (0.42)  
Reassignment (#t+1#)                                                           -0.88***  -0.89*** 
>  -0.81***  -0.87***  -1.49***  -1.41***  -1.08*  
                                                                                (0.24)    (0.23)  
>   (0.21)    (0.25)    (0.31)    (0.34)   (0.49)  
Reassignment (#t+2#)                                                           -0.76**   -0.75**  
>   -0.53*   -0.70**   -1.14***   -0.80*    -0.89  
                                                                                (0.26)    (0.26)  
>   (0.22)    (0.27)    (0.33)    (0.34)   (0.54)  
R2                                                                               0.97      0.97   
>    0.97      0.96      0.97      0.97     0.98   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                  
\midrule                                                                                          
>                                                  
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------


.                  outreg, replay(tab1) append(turnout_pos_req) store(tab_urne_postal_req)

--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
                                                                                 (1)       (2)    
>    (3)       (4)       (5)       (6)      (7)    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                  
\midrule                                                                                          
>                                                  
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
Reassignment (#t-4#)                                                             0.03      0.01   
>    0.00     -0.12      0.02      0.04     0.50   
                                                                                (0.17)    (0.17)  
>   (0.18)    (0.20)    (0.24)    (0.25)   (0.50)  
Reassignment (#t-3#)                                                            -0.07     -0.06   
>   -0.06     -0.04     -0.20      0.02     0.63   
                                                                                (0.17)    (0.17)  
>   (0.17)    (0.21)    (0.25)    (0.26)   (0.52)  
Reassignment (#t-2#)                                                             0.01      0.01   
>    0.01      0.15      0.00      0.19     0.48   
                                                                                (0.12)    (0.12)  
>   (0.12)    (0.14)    (0.20)    (0.21)   (0.42)  
Reassignment (#t+0#)                                                           -1.02***  -1.00*** 
>  -1.02***  -1.07***  -1.31***  -1.57***  -1.37** 
                                                                                (0.23)    (0.23)  
>   (0.23)    (0.24)    (0.33)    (0.37)   (0.42)  
Reassignment (#t+1#)                                                           -0.88***  -0.89*** 
>  -0.81***  -0.87***  -1.49***  -1.41***  -1.08*  
                                                                                (0.24)    (0.23)  
>   (0.21)    (0.25)    (0.31)    (0.34)   (0.49)  
Reassignment (#t+2#)                                                           -0.76**   -0.75**  
>   -0.53*   -0.70**   -1.14***   -0.80*    -0.89  
                                                                                (0.26)    (0.26)  
>   (0.22)    (0.27)    (0.33)    (0.34)   (0.54)  
R2                                                                               0.97      0.97   
>    0.97      0.96      0.97      0.97     0.98   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                  
\midrule                                                                                          
>                                                  
Reassignment (#t-4#)                                                            -0.24     -0.23   
>   -0.21     -0.11     -0.31     -0.18     -0.41  
                                                                                (0.16)    (0.16)  
>   (0.16)    (0.17)    (0.25)    (0.19)   (0.45)  
Reassignment (#t-3#)                                                             0.02      0.01   
>    0.02     -0.11      0.08     -0.04     -0.76  
                                                                                (0.15)    (0.15)  
>   (0.15)    (0.20)    (0.22)    (0.24)   (0.55)  
Reassignment (#t-2#)                                                            -0.07     -0.06   
>   -0.05     -0.17     -0.15     -0.23     -0.26  
                                                                                (0.12)    (0.12)  
>   (0.13)    (0.14)    (0.18)    (0.18)   (0.43)  
Reassignment (#t+0#)                                                            0.66**    0.61**  
>   0.62**    0.54*     0.66*    1.32***    0.66   
                                                                                (0.22)    (0.22)  
>   (0.22)    (0.23)    (0.29)    (0.31)   (0.39)  
Reassignment (#t+1#)                                                           0.93***   0.90***  
>  0.73***   0.87***   1.20***   1.24***   1.35*** 
                                                                                (0.23)    (0.23)  
>   (0.21)    (0.24)    (0.30)    (0.32)   (0.39)  
Reassignment (#t+2#)                                                           1.07***   1.05***  
>   0.71**   0.97***   1.27***   1.28***    1.20*  
                                                                                (0.26)    (0.26)  
>   (0.23)    (0.28)    (0.35)    (0.35)   (0.53)  
R2                                                                               0.96      0.96   
>    0.96      0.95      0.97      0.96     0.96   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------


.                 
.                  outreg, replay(tab_urne_postal_req) store(tab3) ///
>                                 addrow("\midrule" \ "\multicolumn{3}{l}{\textbf{Panel C:} Effect
>  on Total Turnout}" \ "\midrule")  

--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
                                                                                 (1)       (2)    
>    (3)       (4)       (5)       (6)      (7)    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                  
\midrule                                                                                          
>                                                  
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
Reassignment (#t-4#)                                                             0.03      0.01   
>    0.00     -0.12      0.02      0.04     0.50   
                                                                                (0.17)    (0.17)  
>   (0.18)    (0.20)    (0.24)    (0.25)   (0.50)  
Reassignment (#t-3#)                                                            -0.07     -0.06   
>   -0.06     -0.04     -0.20      0.02     0.63   
                                                                                (0.17)    (0.17)  
>   (0.17)    (0.21)    (0.25)    (0.26)   (0.52)  
Reassignment (#t-2#)                                                             0.01      0.01   
>    0.01      0.15      0.00      0.19     0.48   
                                                                                (0.12)    (0.12)  
>   (0.12)    (0.14)    (0.20)    (0.21)   (0.42)  
Reassignment (#t+0#)                                                           -1.02***  -1.00*** 
>  -1.02***  -1.07***  -1.31***  -1.57***  -1.37** 
                                                                                (0.23)    (0.23)  
>   (0.23)    (0.24)    (0.33)    (0.37)   (0.42)  
Reassignment (#t+1#)                                                           -0.88***  -0.89*** 
>  -0.81***  -0.87***  -1.49***  -1.41***  -1.08*  
                                                                                (0.24)    (0.23)  
>   (0.21)    (0.25)    (0.31)    (0.34)   (0.49)  
Reassignment (#t+2#)                                                           -0.76**   -0.75**  
>   -0.53*   -0.70**   -1.14***   -0.80*    -0.89  
                                                                                (0.26)    (0.26)  
>   (0.22)    (0.27)    (0.33)    (0.34)   (0.54)  
R2                                                                               0.97      0.97   
>    0.97      0.96      0.97      0.97     0.98   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                  
\midrule                                                                                          
>                                                  
Reassignment (#t-4#)                                                            -0.24     -0.23   
>   -0.21     -0.11     -0.31     -0.18     -0.41  
                                                                                (0.16)    (0.16)  
>   (0.16)    (0.17)    (0.25)    (0.19)   (0.45)  
Reassignment (#t-3#)                                                             0.02      0.01   
>    0.02     -0.11      0.08     -0.04     -0.76  
                                                                                (0.15)    (0.15)  
>   (0.15)    (0.20)    (0.22)    (0.24)   (0.55)  
Reassignment (#t-2#)                                                            -0.07     -0.06   
>   -0.05     -0.17     -0.15     -0.23     -0.26  
                                                                                (0.12)    (0.12)  
>   (0.13)    (0.14)    (0.18)    (0.18)   (0.43)  
Reassignment (#t+0#)                                                            0.66**    0.61**  
>   0.62**    0.54*     0.66*    1.32***    0.66   
                                                                                (0.22)    (0.22)  
>   (0.22)    (0.23)    (0.29)    (0.31)   (0.39)  
Reassignment (#t+1#)                                                           0.93***   0.90***  
>  0.73***   0.87***   1.20***   1.24***   1.35*** 
                                                                                (0.23)    (0.23)  
>   (0.21)    (0.24)    (0.30)    (0.32)   (0.39)  
Reassignment (#t+2#)                                                           1.07***   1.05***  
>   0.71**   0.97***   1.27***   1.28***    1.20*  
                                                                                (0.26)    (0.26)  
>   (0.23)    (0.28)    (0.35)    (0.35)   (0.53)  
R2                                                                               0.96      0.96   
>    0.96      0.95      0.97      0.96     0.96   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel C:} Effect on Total Turnout}                                     
>                                                  
\midrule                                                                                          
>                                                  
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------


.                  outreg, replay(tab3) append(turnout_tot_req) store(tab_urne_postal_tot_req)

--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
                                                                                 (1)       (2)    
>    (3)       (4)       (5)       (6)      (7)    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                  
\midrule                                                                                          
>                                                  
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
Reassignment (#t-4#)                                                             0.03      0.01   
>    0.00     -0.12      0.02      0.04     0.50   
                                                                                (0.17)    (0.17)  
>   (0.18)    (0.20)    (0.24)    (0.25)   (0.50)  
Reassignment (#t-3#)                                                            -0.07     -0.06   
>   -0.06     -0.04     -0.20      0.02     0.63   
                                                                                (0.17)    (0.17)  
>   (0.17)    (0.21)    (0.25)    (0.26)   (0.52)  
Reassignment (#t-2#)                                                             0.01      0.01   
>    0.01      0.15      0.00      0.19     0.48   
                                                                                (0.12)    (0.12)  
>   (0.12)    (0.14)    (0.20)    (0.21)   (0.42)  
Reassignment (#t+0#)                                                           -1.02***  -1.00*** 
>  -1.02***  -1.07***  -1.31***  -1.57***  -1.37** 
                                                                                (0.23)    (0.23)  
>   (0.23)    (0.24)    (0.33)    (0.37)   (0.42)  
Reassignment (#t+1#)                                                           -0.88***  -0.89*** 
>  -0.81***  -0.87***  -1.49***  -1.41***  -1.08*  
                                                                                (0.24)    (0.23)  
>   (0.21)    (0.25)    (0.31)    (0.34)   (0.49)  
Reassignment (#t+2#)                                                           -0.76**   -0.75**  
>   -0.53*   -0.70**   -1.14***   -0.80*    -0.89  
                                                                                (0.26)    (0.26)  
>   (0.22)    (0.27)    (0.33)    (0.34)   (0.54)  
R2                                                                               0.97      0.97   
>    0.97      0.96      0.97      0.97     0.98   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                  
\midrule                                                                                          
>                                                  
Reassignment (#t-4#)                                                            -0.24     -0.23   
>   -0.21     -0.11     -0.31     -0.18     -0.41  
                                                                                (0.16)    (0.16)  
>   (0.16)    (0.17)    (0.25)    (0.19)   (0.45)  
Reassignment (#t-3#)                                                             0.02      0.01   
>    0.02     -0.11      0.08     -0.04     -0.76  
                                                                                (0.15)    (0.15)  
>   (0.15)    (0.20)    (0.22)    (0.24)   (0.55)  
Reassignment (#t-2#)                                                            -0.07     -0.06   
>   -0.05     -0.17     -0.15     -0.23     -0.26  
                                                                                (0.12)    (0.12)  
>   (0.13)    (0.14)    (0.18)    (0.18)   (0.43)  
Reassignment (#t+0#)                                                            0.66**    0.61**  
>   0.62**    0.54*     0.66*    1.32***    0.66   
                                                                                (0.22)    (0.22)  
>   (0.22)    (0.23)    (0.29)    (0.31)   (0.39)  
Reassignment (#t+1#)                                                           0.93***   0.90***  
>  0.73***   0.87***   1.20***   1.24***   1.35*** 
                                                                                (0.23)    (0.23)  
>   (0.21)    (0.24)    (0.30)    (0.32)   (0.39)  
Reassignment (#t+2#)                                                           1.07***   1.05***  
>   0.71**   0.97***   1.27***   1.28***    1.20*  
                                                                                (0.26)    (0.26)  
>   (0.23)    (0.28)    (0.35)    (0.35)   (0.53)  
R2                                                                               0.96      0.96   
>    0.96      0.95      0.97      0.96     0.96   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel C:} Effect on Total Turnout}                                     
>                                                  
\midrule                                                                                          
>                                                  
Reassignment (#t-4#)                                                            -0.21     -0.22   
>   -0.21     -0.22     -0.29     -0.15     0.09   
                                                                                (0.17)    (0.17)  
>   (0.17)    (0.17)    (0.25)    (0.25)   (0.50)  
Reassignment (#t-3#)                                                            -0.05     -0.05   
>   -0.04     -0.15     -0.12     -0.03     -0.13  
                                                                                (0.16)    (0.16)  
>   (0.16)    (0.17)    (0.23)    (0.27)   (0.48)  
Reassignment (#t-2#)                                                            -0.06     -0.05   
>   -0.04     -0.01     -0.14     -0.04     0.23   
                                                                                (0.13)    (0.13)  
>   (0.13)    (0.15)    (0.21)    (0.24)   (0.35)  
Reassignment (#t+0#)                                                            -0.36*    -0.39*  
>   -0.40*   -0.53**    -0.64*    -0.25    -0.70*  
                                                                                (0.17)    (0.16)  
>   (0.16)    (0.17)    (0.26)    (0.28)   (0.30)  
Reassignment (#t+1#)                                                             0.04      0.01   
>   -0.08     -0.00     -0.28     -0.16     0.27   
                                                                                (0.20)    (0.20)  
>   (0.19)    (0.20)    (0.29)    (0.31)   (0.39)  
Reassignment (#t+2#)                                                             0.31      0.30   
>    0.18      0.27      0.13      0.47     0.31   
                                                                                (0.23)    (0.22)  
>   (0.20)    (0.24)    (0.30)    (0.29)   (0.44)  
R2                                                                               0.99      0.99   
>    0.99      0.99      0.99      0.99     0.99   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------


.         
.         
.         // Display+Export Table
.         outreg using "$tables/Table_C1_ES_bsl", replay(tab_urne_postal_tot_req) replace tex frag
> ment      
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_C1
> _ES_bsl.tex not found)
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
                                                                                 (1)       (2)    
>    (3)       (4)       (5)       (6)      (7)    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                  
\midrule                                                                                          
>                                                  
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------
Reassignment (#t-4#)                                                             0.03      0.01   
>    0.00     -0.12      0.02      0.04     0.50   
                                                                                (0.17)    (0.17)  
>   (0.18)    (0.20)    (0.24)    (0.25)   (0.50)  
Reassignment (#t-3#)                                                            -0.07     -0.06   
>   -0.06     -0.04     -0.20      0.02     0.63   
                                                                                (0.17)    (0.17)  
>   (0.17)    (0.21)    (0.25)    (0.26)   (0.52)  
Reassignment (#t-2#)                                                             0.01      0.01   
>    0.01      0.15      0.00      0.19     0.48   
                                                                                (0.12)    (0.12)  
>   (0.12)    (0.14)    (0.20)    (0.21)   (0.42)  
Reassignment (#t+0#)                                                           -1.02***  -1.00*** 
>  -1.02***  -1.07***  -1.31***  -1.57***  -1.37** 
                                                                                (0.23)    (0.23)  
>   (0.23)    (0.24)    (0.33)    (0.37)   (0.42)  
Reassignment (#t+1#)                                                           -0.88***  -0.89*** 
>  -0.81***  -0.87***  -1.49***  -1.41***  -1.08*  
                                                                                (0.24)    (0.23)  
>   (0.21)    (0.25)    (0.31)    (0.34)   (0.49)  
Reassignment (#t+2#)                                                           -0.76**   -0.75**  
>   -0.53*   -0.70**   -1.14***   -0.80*    -0.89  
                                                                                (0.26)    (0.26)  
>   (0.22)    (0.27)    (0.33)    (0.34)   (0.54)  
R2                                                                               0.97      0.97   
>    0.97      0.96      0.97      0.97     0.98   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                  
\midrule                                                                                          
>                                                  
Reassignment (#t-4#)                                                            -0.24     -0.23   
>   -0.21     -0.11     -0.31     -0.18     -0.41  
                                                                                (0.16)    (0.16)  
>   (0.16)    (0.17)    (0.25)    (0.19)   (0.45)  
Reassignment (#t-3#)                                                             0.02      0.01   
>    0.02     -0.11      0.08     -0.04     -0.76  
                                                                                (0.15)    (0.15)  
>   (0.15)    (0.20)    (0.22)    (0.24)   (0.55)  
Reassignment (#t-2#)                                                            -0.07     -0.06   
>   -0.05     -0.17     -0.15     -0.23     -0.26  
                                                                                (0.12)    (0.12)  
>   (0.13)    (0.14)    (0.18)    (0.18)   (0.43)  
Reassignment (#t+0#)                                                            0.66**    0.61**  
>   0.62**    0.54*     0.66*    1.32***    0.66   
                                                                                (0.22)    (0.22)  
>   (0.22)    (0.23)    (0.29)    (0.31)   (0.39)  
Reassignment (#t+1#)                                                           0.93***   0.90***  
>  0.73***   0.87***   1.20***   1.24***   1.35*** 
                                                                                (0.23)    (0.23)  
>   (0.21)    (0.24)    (0.30)    (0.32)   (0.39)  
Reassignment (#t+2#)                                                           1.07***   1.05***  
>   0.71**   0.97***   1.27***   1.28***    1.20*  
                                                                                (0.26)    (0.26)  
>   (0.23)    (0.28)    (0.35)    (0.35)   (0.53)  
R2                                                                               0.96      0.96   
>    0.96      0.95      0.97      0.96     0.96   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
\midrule                                                                                          
>                                                  
\multicolumn{3}{l}{\textbf{Panel C:} Effect on Total Turnout}                                     
>                                                  
\midrule                                                                                          
>                                                  
Reassignment (#t-4#)                                                            -0.21     -0.22   
>   -0.21     -0.22     -0.29     -0.15     0.09   
                                                                                (0.17)    (0.17)  
>   (0.17)    (0.17)    (0.25)    (0.25)   (0.50)  
Reassignment (#t-3#)                                                            -0.05     -0.05   
>   -0.04     -0.15     -0.12     -0.03     -0.13  
                                                                                (0.16)    (0.16)  
>   (0.16)    (0.17)    (0.23)    (0.27)   (0.48)  
Reassignment (#t-2#)                                                            -0.06     -0.05   
>   -0.04     -0.01     -0.14     -0.04     0.23   
                                                                                (0.13)    (0.13)  
>   (0.13)    (0.15)    (0.21)    (0.24)   (0.35)  
Reassignment (#t+0#)                                                            -0.36*    -0.39*  
>   -0.40*   -0.53**    -0.64*    -0.25    -0.70*  
                                                                                (0.17)    (0.16)  
>   (0.16)    (0.17)    (0.26)    (0.28)   (0.30)  
Reassignment (#t+1#)                                                             0.04      0.01   
>   -0.08     -0.00     -0.28     -0.16     0.27   
                                                                                (0.20)    (0.20)  
>   (0.19)    (0.20)    (0.29)    (0.31)   (0.39)  
Reassignment (#t+2#)                                                             0.31      0.30   
>    0.18      0.27      0.13      0.47     0.31   
                                                                                (0.23)    (0.22)  
>   (0.20)    (0.24)    (0.30)    (0.29)   (0.44)  
R2                                                                               0.99      0.99   
>    0.99      0.99      0.99      0.99     0.99   
N                                                                               4,666     4,666   
>   4,944     4,666     2,040     3,456     3,082  
#treated precincts                                                               280       280    
>    280       280       150       94        54    
#control precincts                                                               338       338    
>    338       338       105       338      338    
Precinct FE                                                                       X         X     
>     X         X         X         X        X     
Election-District FE                                                              X         X     
>     X                   X         X        X     
Weights                                                                                     X     
>     X         X         X         X        X     
Full sample                                                                                       
>     X                                            
Election FE                                                                                       
>               X                                  
Clean sample                                                                                      
>                         X                        
Balanced sample                                                                                   
>                                   X              
No boundary change                                                                                
>                                             X    
--------------------------------------------------------------------------------------------------
> ---------------------------------------------------


.         // clean .tex table for Overleaf 
.         cleantex "$tables/Table_C1_ES_bsl.tex" , nodisplay replace

.         
. 
. 
. ********************************************************************************
. *        Event Study Table CONDITIONAL on log street distance (Table 1)
. ********************************************************************************
.         
.         * TABLE 1. Event Study Estimates Conditional on Log Walking Distance
. 
.         global order  F4event F3event F2event /*F1event*/ L0event L1event L2event 

.         
. outreg, clear                   

. foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                         
.         *gen leads and lags: 100%
.         cap drop L* F*
  3.         forvalues l = 7(-1)1 {
  4.                 gen     F`l'event = K==-`l'
  5.                 lab var F`l'event "Reassignment (#t-`l'#)"
  6.         }       
  7.         forvalues l = 0/7 {
  8.                 gen     L`l'event = K==`l'
  9.                 lab var L`l'event "Reassignment (#t+`l'#)"
 10.         }               
 11.         drop    F1event // drop reference period
 12.         
.         // replicate baseline (no dist control) for comparision [not in output, just for computi
> ng fraction explained]
.          reghdfe `v' F* L*      $ctr    if smpl_trim==1         $wgt, absorb(i.wahl_id#i.stadtbe
> z i.sb_new) cluster(sb_new) 
 13.                 estimates store `v'b_fe_de
 14. 
.          reghdfe `v'  F* L*     $ctr    if smpl_trim==1         $wgt, absorb(i.wahl_id i.sb_new)
>  cluster(sb_new) 
 15.                 estimates store `v'b_fe_e
 16. 
.                 
.                 // (1) Event study  -- ELECTION-DISTRICT FE + Controlling for distance
.                  reghdfe `v' ln_street_dist F* L*       $ctr    if smpl_trim==1         $wgt, ab
> sorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
 17.                  outreg,  $opt  keep(ln_street_dist $order)     addrow(Precinct FE, X \ Elect
> ion-District FE, X \ Controls, X)  
 18.                 estimates store `v'_fe_de
 19.                         
.         
.                 // (2) Event study  -- ONLY ELECTION FE + Controlling for distance
.                  reghdfe `v' ln_street_dist F* L*       $ctr    if smpl_trim==1         $wgt, ab
> sorb(i.wahl_id i.sb_new) cluster(sb_new) 
 20.                  outreg,  $opt  keep(ln_street_dist $order)     addrow(Precinct FE, X \ Elect
> ion FE, X \ Controls, X)
 21.                 estimates store `v'_fe_e
 22.                 
. }               
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      17.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9658
                                                  Within R-sq.    =     0.1691
Number of clusters (sb_new)  =        618         Root MSE        =     1.6979

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3609916    -0.46   0.646    -.8749677    .5428746
          F6event |   .0365542   .3229698     0.11   0.910    -.5976991    .6708075
          F5event |   .2427259   .2553086     0.95   0.342    -.2586533    .7441051
          F4event |   .0132213   .1747424     0.08   0.940    -.3299406    .3563833
          F3event |  -.0565081    .169943    -0.33   0.740    -.3902449    .2772287
          F2event |    .006289   .1214372     0.05   0.959    -.2321913    .2447694
          L0event |  -.9985596   .2347416    -4.25   0.000    -1.459549   -.5375702
          L1event |   -.892731   .2344548    -3.81   0.000    -1.353157   -.4323048
          L2event |  -.7515249   .2578216    -2.91   0.004    -1.257839   -.2452106
          L3event |  -.2931705   .2663112    -1.10   0.271    -.8161567    .2298157
          L4event |  -.8782313   .4731179    -1.86   0.064    -1.807348    .0508853
          L5event |  -.5487131   .6172865    -0.89   0.374     -1.76095    .6635241
          L6event |    1.07717   .7208125     1.49   0.136    -.3383731    2.492714
          L7event |   .9427971   1.187553     0.79   0.428    -1.389339    3.274933
        ln_ew_ges |  -.7878053   .9548651    -0.83   0.410    -2.662985    1.087374
         ew_biodt |    .373603   .0281093    13.29   0.000     .3184015    .4288045
        ew_dtmihi |   .0630129   .0513196     1.23   0.220    -.0377693    .1637951
         ew_ledig |   .2127399   .0574696     3.70   0.000     .0998801    .3255996
       ew_married |   .4312672   .0590852     7.30   0.000     .3152348    .5472997
        wb_anteil |  -.2895078   .0204524   -14.16   0.000    -.3296726   -.2493431
          wb_ausl |   .0153767   .0162706     0.95   0.345    -.0165758    .0473293
         wb_18t24 |  -.0164981   .0295896    -0.56   0.577    -.0746067    .0416105
         wb_25t34 |  -.0724461   .0193717    -3.74   0.000    -.1104886   -.0344037
         wb_35t44 |    .006101   .0230068     0.27   0.791    -.0390802    .0512822
         wb_45t59 |   .0140084   .0220328     0.64   0.525      -.02926    .0572768
          avg_dur |  -.0288813   .0210954    -1.37   0.171    -.0703088    .0125462
          hh_kids |  -.0506236   .0408723    -1.24   0.216    -.1308893    .0296421
mpreis_flats_rent |   .0303932   .0255105     1.19   0.234    -.0197047    .0804911
            _cons |   11.36038   9.043266     1.26   0.210    -6.398933    29.11969
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.69
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9633
                                                  Adj R-squared   =     0.9573
                                                  Within R-sq.    =     0.1478
Number of clusters (sb_new)  =        618         Root MSE        =     1.8963

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.2719899   .3783767    -0.72   0.473    -1.015052    .4710724
          F6event |  -.1600481   .3381397    -0.47   0.636    -.8240923    .5039962
          F5event |    .106833   .2699344     0.40   0.692    -.4232686    .6369345
          F4event |  -.1151478   .1961054    -0.59   0.557    -.5002627    .2699671
          F3event |  -.0375634   .2066195    -0.18   0.856     -.443326    .3681993
          F2event |   .1549506   .1416316     1.09   0.274    -.1231878     .433089
          L0event |  -1.068154   .2422044    -4.41   0.000    -1.543799   -.5925095
          L1event |  -.8724909   .2540048    -3.43   0.001     -1.37131   -.3736721
          L2event |  -.7015394    .270616    -2.59   0.010     -1.23298   -.1700993
          L3event |  -.0880174   .2711036    -0.32   0.746     -.620415    .4443802
          L4event |  -.1994726   .4709713    -0.42   0.672    -1.124374    .7254285
          L5event |   .4861285   .7253154     0.67   0.503    -.9382577    1.910515
          L6event |   .4275625   .8684947     0.49   0.623    -1.278002    2.133127
          L7event |   .9091168   1.138177     0.80   0.425    -1.326054    3.144287
        ln_ew_ges |  -.7462869   1.122614    -0.66   0.506    -2.950895    1.458321
         ew_biodt |   .3520456   .0289625    12.16   0.000     .2951686    .4089226
        ew_dtmihi |   .0613483   .0527968     1.16   0.246    -.0423348    .1650315
         ew_ledig |    .222933   .0597345     3.73   0.000     .1056254    .3402406
       ew_married |   .4248988    .061581     6.90   0.000      .303965    .5458326
        wb_anteil |  -.2432859   .0196388   -12.39   0.000    -.2818528   -.2047189
          wb_ausl |   .0212218   .0149923     1.42   0.157    -.0082203    .0506639
         wb_18t24 |  -.0462596   .0292086    -1.58   0.114      -.10362    .0111007
         wb_25t34 |  -.0432859   .0168545    -2.57   0.010    -.0763851   -.0101868
         wb_35t44 |    .002344   .0215326     0.11   0.913     -.039942    .0446301
         wb_45t59 |    .015456   .0219157     0.71   0.481    -.0275824    .0584944
          avg_dur |  -.0193425   .0223354    -0.87   0.387     -.063205      .02452
          hh_kids |  -.0496397   .0436321    -1.14   0.256    -.1353252    .0360458
mpreis_flats_rent |   .0512944   .0196556     2.61   0.009     .0126944    .0898944
            _cons |   8.087133   10.18894     0.79   0.428    -11.92207    28.09634
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      25.23
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9754
                                                  Adj R-squared   =     0.9699
                                                  Within R-sq.    =     0.2695
Number of clusters (sb_new)  =        618         Root MSE        =     1.5922

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
   ln_street_dist |  -3.389038   .2516279   -13.47   0.000    -3.883189   -2.894887
          F7event |  -.0756156   .3294278    -0.23   0.819    -.7225513    .5713201
          F6event |   .0289247    .292182     0.10   0.921    -.5448671    .6027165
          F5event |   .2183375   .2406509     0.91   0.365    -.2542566    .6909317
          F4event |   .0103551   .1683978     0.06   0.951    -.3203473    .3410575
          F3event |  -.0954896   .1664874    -0.57   0.566    -.4224403    .2314611
          F2event |   .0236705   .1191576     0.20   0.843    -.2103331    .2576741
          L0event |  -.5832283   .2070645    -2.82   0.005     -.989865   -.1765916
          L1event |  -.6282211   .2012612    -3.12   0.002    -1.023461   -.2329811
          L2event |   -.434162    .228963    -1.90   0.058    -.8838032    .0154792
          L3event |  -.3501167   .2473372    -1.42   0.157    -.8358414     .135608
          L4event |  -.5822196   .4241971    -1.37   0.170    -1.415265    .2508256
          L5event |  -.3942589   .5682881    -0.69   0.488    -1.510272    .7217545
          L6event |    1.27796   .6622212     1.93   0.054     -.022521    2.578441
          L7event |   .8256023   1.234719     0.67   0.504    -1.599158    3.250363
        ln_ew_ges |  -1.486649   .8450907    -1.76   0.079    -3.146252     .172954
         ew_biodt |   .3721571   .0260588    14.28   0.000     .3209824    .4233318
        ew_dtmihi |   .0334341    .048465     0.69   0.491    -.0617422    .1286105
         ew_ledig |   .2431458   .0499261     4.87   0.000     .1451001    .3411915
       ew_married |   .4217038   .0524151     8.05   0.000     .3187701    .5246376
        wb_anteil |  -.2898507   .0190412   -15.22   0.000    -.3272441   -.2524574
          wb_ausl |   .0180574   .0148153     1.22   0.223    -.0110371    .0471519
         wb_18t24 |  -.0091914   .0282614    -0.33   0.745    -.0646916    .0463087
         wb_25t34 |  -.0509448   .0176953    -2.88   0.004    -.0856951   -.0161946
         wb_35t44 |   .0017585    .021118     0.08   0.934    -.0397135    .0432305
         wb_45t59 |   .0168576   .0206382     0.82   0.414    -.0236721    .0573872
          avg_dur |  -.0217453   .0204117    -1.07   0.287    -.0618301    .0183395
          hh_kids |  -.0267541   .0380322    -0.70   0.482    -.1014425    .0479342
mpreis_flats_rent |   .0382438     .02399     1.59   0.111    -.0088681    .0853558
            _cons |   13.39303   7.890264     1.70   0.090    -2.101996    28.88806
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: no existing table found for merge or append

                    ---------------------------------------------------------
                                            Effect on polling place turnout 
                    ---------------------------------------------------------
                     Log walking distance              -3.39***             
                                                        (0.25)              
                     Reassignment (#t-4#)                0.01               
                                                        (0.17)              
                     Reassignment (#t-3#)                -0.10              
                                                        (0.17)              
                     Reassignment (#t-2#)                0.02               
                                                        (0.12)              
                     Reassignment (#t+0#)               -0.58**             
                                                        (0.21)              
                     Reassignment (#t+1#)               -0.63**             
                                                        (0.20)              
                     Reassignment (#t+2#)                -0.43              
                                                        (0.23)              
                     R2                                  0.98               
                     N                                   4,666              
                     Precinct FE                          X                 
                     Election-District FE                 X                 
                     Controls                              X                
                    ---------------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      23.49
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9670
                                                  Adj R-squared   =     0.9616
                                                  Within R-sq.    =     0.2338
Number of clusters (sb_new)  =        618         Root MSE        =     1.7983

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
   ln_street_dist |   -3.42898    .255426   -13.42   0.000    -3.930589    -2.92737
          F7event |  -.2382187    .356667    -0.67   0.504    -.9386472    .4622098
          F6event |  -.2137879   .3127797    -0.68   0.495    -.8280298    .4004539
          F5event |   .0249503   .2559896     0.10   0.922    -.4777662    .5276669
          F4event |  -.1492519   .1892787    -0.79   0.431    -.5209605    .2224566
          F3event |  -.0960296   .2044625    -0.47   0.639    -.4975563    .3054972
          F2event |    .162986    .139737     1.17   0.244    -.1114319    .4374038
          L0event |  -.6642396   .2228509    -2.98   0.003    -1.101878   -.2266013
          L1event |  -.6365066   .2254085    -2.82   0.005    -1.079168   -.1938457
          L2event |  -.4368401   .2393423    -1.83   0.068    -.9068645    .0331842
          L3event |  -.1444955   .2467509    -0.59   0.558    -.6290689    .3400779
          L4event |  -.1542154   .4606626    -0.33   0.738    -1.058872    .7504413
          L5event |   .4794252   .6752909     0.71   0.478    -.8467221    1.805572
          L6event |   .5574349   .8470786     0.66   0.511    -1.106072    2.220942
          L7event |   .9113192   1.202976     0.76   0.449    -1.451105    3.273744
        ln_ew_ges |  -1.302402   .9770042    -1.33   0.183    -3.221059    .6162548
         ew_biodt |   .3517144     .02714    12.96   0.000     .2984164    .4050125
        ew_dtmihi |   .0222568   .0501546     0.44   0.657    -.0762376    .1207513
         ew_ledig |   .2465072   .0534549     4.61   0.000     .1415316    .3514828
       ew_married |   .4134812   .0561785     7.36   0.000     .3031569    .5238054
        wb_anteil |  -.2453238   .0186414   -13.16   0.000     -.281932   -.2087155
          wb_ausl |   .0228956   .0143597     1.59   0.111    -.0053042    .0510954
         wb_18t24 |    -.03774   .0279976    -1.35   0.178    -.0927221    .0172421
         wb_25t34 |  -.0305538   .0153768    -1.99   0.047     -.060751   -.0003567
         wb_35t44 |  -.0038983   .0205017    -0.19   0.849    -.0441599    .0363632
         wb_45t59 |   .0148287   .0204633     0.72   0.469    -.0253575     .055015
          avg_dur |  -.0159716   .0214671    -0.74   0.457    -.0581291    .0261859
          hh_kids |  -.0168247   .0396639    -0.42   0.672    -.0947173    .0610679
mpreis_flats_rent |    .058033   .0195054     2.98   0.003      .019728    .0963379
            _cons |   9.839251   8.918452     1.10   0.270    -7.674951    27.35345
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Log walking distance              -3.39***                         -3.43***             
                                        (0.25)                           (0.26)              
     Reassignment (#t-4#)                0.01                             -0.15              
                                        (0.17)                           (0.19)              
     Reassignment (#t-3#)                -0.10                            -0.10              
                                        (0.17)                           (0.20)              
     Reassignment (#t-2#)                0.02                             0.16               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)               -0.58**                          -0.66**             
                                        (0.21)                           (0.22)              
     Reassignment (#t+1#)               -0.63**                          -0.64**             
                                        (0.20)                           (0.23)              
     Reassignment (#t+2#)                -0.43                            -0.44              
                                        (0.23)                           (0.24)              
     R2                                  0.98                             0.97               
     N                                   4,666                            4,666              
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Controls                              X                                X                
     Election FE                                                           X                 
    ------------------------------------------------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9616
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2040
Number of clusters (sb_new)  =        618         Root MSE        =     1.6847

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3314703     0.25   0.801    -.5673098    .7345839
          F6event |   .2184141   .2600152     0.84   0.401    -.2922081    .7290362
          F5event |  -.4796951   .2637694    -1.82   0.069    -.9976898    .0382995
          F4event |  -.2325114   .1614186    -1.44   0.150    -.5495078     .084485
          F3event |   .0074853   .1524588     0.05   0.961    -.2919157    .3068862
          F2event |   -.059996   .1248273    -0.48   0.631    -.3051339    .1851419
          L0event |   .6092887   .2189284     2.78   0.006     .1793535    1.039224
          L1event |   .9038027   .2273231     3.98   0.000      .457382    1.350223
          L2event |   1.047491   .2631015     3.98   0.000     .5308081    1.564174
          L3event |   .4175165   .2577372     1.62   0.106    -.0886321    .9236651
          L4event |   1.531036   .6502778     2.35   0.019     .2540104    2.808063
          L5event |   2.356691   .5459981     4.32   0.000     1.284451    3.428931
          L6event |  -.3848372    .877942    -0.44   0.661    -2.108954     1.33928
          L7event |   -.503072    .765562    -0.66   0.511    -2.006495    1.000351
        ln_ew_ges |   2.465914   1.349016     1.83   0.068    -.1833058    5.115133
         ew_biodt |    .388145   .0296323    13.10   0.000     .3299526    .4463375
        ew_dtmihi |  -.2303872   .0595829    -3.87   0.000     -.347397   -.1133774
         ew_ledig |   .2111155   .0802108     2.63   0.009     .0535962    .3686348
       ew_married |   .2074512   .0799261     2.60   0.010     .0504911    .3644114
        wb_anteil |  -.2425344   .0223631   -10.85   0.000    -.2864513   -.1986174
          wb_ausl |   -.068913   .0145836    -4.73   0.000    -.0975526   -.0402734
         wb_18t24 |  -.0273058   .0275422    -0.99   0.322    -.0813937    .0267821
         wb_25t34 |   .0526598   .0194716     2.70   0.007     .0144212    .0908985
         wb_35t44 |  -.0089296   .0249211    -0.36   0.720    -.0578702    .0400109
         wb_45t59 |   -.038132   .0205362    -1.86   0.064    -.0784614    .0021973
          avg_dur |   .0459808   .0233899     1.97   0.050     .0000472    .0919144
          hh_kids |  -.0642629   .0417916    -1.54   0.125    -.1463339    .0178081
mpreis_flats_rent |  -.0155286   .0234069    -0.66   0.507    -.0614954    .0304383
            _cons |  -11.72708   11.35784    -1.03   0.302     -34.0318    10.57764
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      15.68
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9457
                                                  Adj R-squared   =     0.9369
                                                  Within R-sq.    =     0.2003
Number of clusters (sb_new)  =        618         Root MSE        =     1.9547

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .276988   .3092727     0.90   0.371    -.3303667    .8843427
          F6event |   .3148414   .2770009     1.14   0.256    -.2291375    .8588203
          F5event |  -.3360842   .2820305    -1.19   0.234    -.8899403    .2177719
          F4event |  -.1089699   .1694836    -0.64   0.520    -.4418045    .2238647
          F3event |  -.1143589   .2016849    -0.57   0.571    -.5104309    .2817132
          F2event |  -.1662226   .1429373    -1.16   0.245    -.4469252    .1144801
          L0event |   .5381031   .2339631     2.30   0.022     .0786427    .9975636
          L1event |   .8706087   .2415275     3.60   0.000     .3962931    1.344924
          L2event |   .9679545   .2802916     3.45   0.001     .4175133    1.518396
          L3event |   .0769893    .269978     0.29   0.776    -.4531979    .6071765
          L4event |   1.629122   .6201424     2.63   0.009     .4112765    2.846968
          L5event |   .9910967   .5551212     1.79   0.075    -.0990593    2.081253
          L6event |   .0056946   .5736399     0.01   0.992    -1.120829    1.132218
          L7event |   -.400396   .9271444    -0.43   0.666    -2.221137    1.420345
        ln_ew_ges |   2.396505   1.412975     1.70   0.090    -.3783192    5.171328
         ew_biodt |   .4381682   .0312317    14.03   0.000     .3768348    .4995016
        ew_dtmihi |  -.2278822    .060953    -3.74   0.000    -.3475827   -.1081817
         ew_ledig |     .16008   .0853711     1.88   0.061    -.0075731     .327733
       ew_married |   .1907753   .0848466     2.25   0.025     .0241523    .3573984
        wb_anteil |  -.2980442    .026516   -11.24   0.000    -.3501167   -.2459717
          wb_ausl |   -.056026   .0139204    -4.02   0.000    -.0833631    -.028689
         wb_18t24 |  -.0102823   .0285045    -0.36   0.718    -.0662599    .0456953
         wb_25t34 |   .0315455   .0168646     1.87   0.062    -.0015734    .0646644
         wb_35t44 |  -.0088774   .0224818    -0.39   0.693    -.0530276    .0352729
         wb_45t59 |   -.027485   .0205433    -1.34   0.181    -.0678283    .0128583
          avg_dur |   .0609851   .0243414     2.51   0.012      .013183    .1087872
          hh_kids |  -.0405334   .0434366    -0.93   0.351    -.1258349    .0447681
mpreis_flats_rent |   .0523958   .0213153     2.46   0.014     .0105364    .0942552
            _cons |  -9.380828    12.1834    -0.77   0.442    -33.30678    14.54513
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      22.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9644
                                                  Adj R-squared   =     0.9565
                                                  Within R-sq.    =     0.2619
Number of clusters (sb_new)  =        618         Root MSE        =     1.6225

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
   ln_street_dist |   2.608626   .2487749    10.49   0.000     2.120078    3.097174
          F7event |   .0140301   .3280467     0.04   0.966    -.6301934    .6582535
          F6event |   .2242867   .2481602     0.90   0.366    -.2630543    .7116277
          F5event |  -.4609228   .2587552    -1.78   0.075    -.9690706    .0472249
          F4event |  -.2303052   .1600835    -1.44   0.151    -.5446799    .0840694
          F3event |   .0374903   .1508112     0.25   0.804    -.2586752    .3336557
          F2event |  -.0733749   .1229165    -0.60   0.551    -.3147604    .1680105
          L0event |   .2895979   .2043257     1.42   0.157    -.1116603    .6908561
          L1event |   .7002029   .2028135     3.45   0.001     .3019144    1.098491
          L2event |   .8032091   .2360579     3.40   0.001     .3396348    1.266783
          L3event |   .4613494   .2384438     1.93   0.053    -.0069105    .9296092
          L4event |   1.303189   .6294359     2.07   0.039     .0670926    2.539285
          L5event |   2.237804   .5110066     4.38   0.000     1.234281    3.241327
          L6event |  -.5393899   .8353565    -0.65   0.519    -2.179877    1.101097
          L7event |  -.4128643   .7199434    -0.57   0.567    -1.826701    1.000972
        ln_ew_ges |   3.003831   1.280165     2.35   0.019     .4898221     5.51784
         ew_biodt |   .3892579   .0280544    13.88   0.000     .3341642    .4443517
        ew_dtmihi |  -.2076197   .0577885    -3.59   0.000    -.3211058   -.0941336
         ew_ledig |   .1877113   .0713292     2.63   0.009     .0476339    .3277888
       ew_married |   .2148124   .0733007     2.93   0.004     .0708633    .3587615
        wb_anteil |  -.2422704     .02123   -11.41   0.000    -.2839622   -.2005786
          wb_ausl |  -.0709764   .0139885    -5.07   0.000    -.0984472   -.0435055
         wb_18t24 |  -.0329299   .0271733    -1.21   0.226    -.0862932    .0204334
         wb_25t34 |   .0361097   .0179309     2.01   0.044     .0008968    .0713226
         wb_35t44 |  -.0055871   .0231381    -0.24   0.809     -.051026    .0398518
         wb_45t59 |  -.0403251   .0194761    -2.07   0.039    -.0785725   -.0020777
          avg_dur |    .040488   .0222614     1.82   0.069    -.0032292    .0842053
          hh_kids |  -.0826358   .0409616    -2.02   0.044    -.1630769   -.0021948
mpreis_flats_rent |  -.0215714   .0225599    -0.96   0.339    -.0658749    .0227321
            _cons |  -13.29167    10.6666    -1.25   0.213    -34.23891    7.655576
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Log walking distance              -3.39***                         -3.43***             
                                        (0.25)                           (0.26)              
     Reassignment (#t-4#)                0.01                             -0.15              
                                        (0.17)                           (0.19)              
     Reassignment (#t-3#)                -0.10                            -0.10              
                                        (0.17)                           (0.20)              
     Reassignment (#t-2#)                0.02                             0.16               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)               -0.58**                          -0.66**             
                                        (0.21)                           (0.22)              
     Reassignment (#t+1#)               -0.63**                          -0.64**             
                                        (0.20)                           (0.23)              
     Reassignment (#t+2#)                -0.43                            -0.44              
                                        (0.23)                           (0.24)              
     R2                                  0.98                             0.97               
     N                                   4,666                            4,666              
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Controls                              X                                X                
     Election FE                                                           X                 
    ------------------------------------------------------------------------------------------


                       ---------------------------------------------------
                                               Effect on mail-in turnout 
                       ---------------------------------------------------
                        Log walking distance            2.61***          
                                                        (0.25)           
                        Reassignment (#t-4#)             -0.23           
                                                        (0.16)           
                        Reassignment (#t-3#)             0.04            
                                                        (0.15)           
                        Reassignment (#t-2#)             -0.07           
                                                        (0.12)           
                        Reassignment (#t+0#)             0.29            
                                                        (0.20)           
                        Reassignment (#t+1#)            0.70***          
                                                        (0.20)           
                        Reassignment (#t+2#)            0.80***          
                                                        (0.24)           
                        R2                               0.96            
                        N                                4,666           
                        Precinct FE                       X              
                        Election-District FE              X              
                        Controls                           X             
                        Election FE                                      
                       ---------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      21.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9487
                                                  Adj R-squared   =     0.9404
                                                  Within R-sq.    =     0.2447
Number of clusters (sb_new)  =        618         Root MSE        =     1.8999

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
   ln_street_dist |   2.622052   .2566932    10.21   0.000     2.117954     3.12615
          F7event |   .2511641   .3178198     0.79   0.430    -.3729756    .8753038
          F6event |   .3559349   .2714134     1.31   0.190    -.1770712    .8889409
          F5event |  -.2734707   .2757317    -0.99   0.322    -.8149571    .2680158
          F4event |  -.0828914   .1675184    -0.49   0.621    -.4118667     .246084
          F3event |  -.0696513   .2004904    -0.35   0.728    -.4633775    .3240749
          F2event |   -.172367   .1419061    -1.21   0.225    -.4510446    .1063106
          L0event |     .22924   .2261913     1.01   0.311    -.2149582    .6734381
          L1event |   .6901577   .2188145     3.15   0.002     .2604462    1.119869
          L2event |   .7655458   .2562998     2.99   0.003       .26222    1.268872
          L3event |   .1201766   .2477701     0.49   0.628    -.3663983    .6067516
          L4event |   1.594515   .5964776     2.67   0.008     .4231428    2.765888
          L5event |   .9962226    .525721     1.89   0.059    -.0361968    2.028642
          L6event |  -.0936155   .5486369    -0.17   0.865    -1.171038    .9838067
          L7event |  -.4020801   .9380435    -0.43   0.668    -2.244225    1.440065
        ln_ew_ges |   2.821751   1.343357     2.10   0.036     .1836448    5.459858
         ew_biodt |   .4384215    .029803    14.71   0.000     .3798938    .4969492
        ew_dtmihi |  -.1979899   .0592522    -3.34   0.001    -.3143504   -.0816295
         ew_ledig |   .1420534   .0757689     1.87   0.061    -.0067427    .2908495
       ew_married |   .1995061    .077237     2.58   0.010     .0478268    .3511854
        wb_anteil |  -.2964858   .0255655   -11.60   0.000    -.3466918   -.2462799
          wb_ausl |   -.057306   .0136281    -4.20   0.000     -.084069   -.0305429
         wb_18t24 |   -.016797   .0278605    -0.60   0.547      -.07151    .0379159
         wb_25t34 |   .0218096    .015635     1.39   0.164    -.0088946    .0525138
         wb_35t44 |   -.004104   .0213259    -0.19   0.847    -.0459841    .0377761
         wb_45t59 |  -.0270054   .0195061    -1.38   0.167    -.0653117    .0113009
          avg_dur |   .0584075   .0232187     2.52   0.012     .0128102    .1040047
          hh_kids |  -.0656262   .0430168    -1.53   0.128    -.1501033     .018851
mpreis_flats_rent |    .047243    .020952     2.25   0.024     .0060971    .0883888
            _cons |  -10.72063   11.56172    -0.93   0.354    -33.42572    11.98447
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Log walking distance              -3.39***                         -3.43***             
                                        (0.25)                           (0.26)              
     Reassignment (#t-4#)                0.01                             -0.15              
                                        (0.17)                           (0.19)              
     Reassignment (#t-3#)                -0.10                            -0.10              
                                        (0.17)                           (0.20)              
     Reassignment (#t-2#)                0.02                             0.16               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)               -0.58**                          -0.66**             
                                        (0.21)                           (0.22)              
     Reassignment (#t+1#)               -0.63**                          -0.64**             
                                        (0.20)                           (0.23)              
     Reassignment (#t+2#)                -0.43                            -0.44              
                                        (0.23)                           (0.24)              
     R2                                  0.98                             0.97               
     N                                   4,666                            4,666              
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Controls                              X                                X                
     Election FE                                                           X                 
    ------------------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Log walking distance            2.61***                    2.62***          
                                           (0.25)                     (0.26)           
           Reassignment (#t-4#)             -0.23                      -0.08           
                                           (0.16)                     (0.17)           
           Reassignment (#t-3#)             0.04                       -0.07           
                                           (0.15)                     (0.20)           
           Reassignment (#t-2#)             -0.07                      -0.17           
                                           (0.12)                     (0.14)           
           Reassignment (#t+0#)             0.29                       0.23            
                                           (0.20)                     (0.23)           
           Reassignment (#t+1#)            0.70***                    0.69**           
                                           (0.20)                     (0.22)           
           Reassignment (#t+2#)            0.80***                    0.77**           
                                           (0.24)                     (0.26)           
           R2                               0.96                       0.95            
           N                                4,666                      4,666           
           Precinct FE                       X                          X              
           Election-District FE              X                                         
           Controls                           X                          X             
           Election FE                                                  X              
          ------------------------------------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4395
Number of clusters (sb_new)  =        618         Root MSE        =     1.6245

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3259399    -0.25   0.800    -.7224958    .5576764
          F6event |   .2549675   .2761707     0.92   0.356    -.2873811    .7973161
          F5event |  -.2369689   .2619342    -0.90   0.366    -.7513594    .2774217
          F4event |  -.2192898   .1694872    -1.29   0.196    -.5521315     .113552
          F3event |  -.0490231   .1579758    -0.31   0.756    -.3592586    .2612124
          F2event |  -.0537065   .1322374    -0.41   0.685    -.3133964    .2059834
          L0event |  -.3892706   .1633916    -2.38   0.018    -.7101417   -.0683996
          L1event |    .011072    .200952     0.06   0.956    -.3835608    .4057048
          L2event |   .2959665    .224252     1.32   0.187    -.1444233    .7363563
          L3event |   .1243464   .2332215     0.53   0.594    -.3336578    .5823505
          L4event |   .6528057   .6207241     1.05   0.293    -.5661824    1.871794
          L5event |   1.807978   .7132727     2.53   0.011      .407241    3.208714
          L6event |   .6923315   .7952045     0.87   0.384     -.869304    2.253967
          L7event |   .4397263   1.099927     0.40   0.689    -1.720328    2.599781
        ln_ew_ges |   1.678109   1.068642     1.57   0.117    -.4205078    3.776725
         ew_biodt |    .761748   .0314252    24.24   0.000     .7000348    .8234612
        ew_dtmihi |  -.1673742   .0522282    -3.20   0.001    -.2699407   -.0648076
         ew_ledig |   .4238555   .0699086     6.06   0.000     .2865679    .5611432
       ew_married |   .6387186   .0688582     9.28   0.000     .5034938    .7739433
        wb_anteil |  -.5320422   .0239517   -22.21   0.000     -.579079   -.4850055
          wb_ausl |  -.0535363   .0175921    -3.04   0.002     -.088084   -.0189885
         wb_18t24 |  -.0438039    .025903    -1.69   0.091    -.0946727    .0070648
         wb_25t34 |  -.0197863   .0166674    -1.19   0.236     -.052518    .0129454
         wb_35t44 |  -.0028286   .0208991    -0.14   0.892    -.0438706    .0382134
         wb_45t59 |  -.0241236   .0196706    -1.23   0.221    -.0627531    .0145059
          avg_dur |   .0170995    .022054     0.78   0.438    -.0262106    .0604096
          hh_kids |  -.1148866    .035755    -3.21   0.001    -.1851028   -.0446704
mpreis_flats_rent |   .0148646   .0236111     0.63   0.529    -.0315033    .0612326
            _cons |   -.366715   10.03193    -0.04   0.971    -20.06759    19.33416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      45.00
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9875
                                                  Adj R-squared   =     0.9855
                                                  Within R-sq.    =     0.4457
Number of clusters (sb_new)  =        618         Root MSE        =     1.7997

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0049977   .3288787     0.02   0.988    -.6408596     .650855
          F6event |   .1547925   .2763992     0.56   0.576    -.3880047    .6975897
          F5event |  -.2292509   .2737864    -0.84   0.403     -.766917    .3084152
          F4event |  -.2241174   .1715177    -1.31   0.192    -.5609465    .1127118
          F3event |  -.1519224   .1677705    -0.91   0.366    -.4813929    .1775481
          F2event |  -.0112714   .1532364    -0.07   0.941    -.3121996    .2896568
          L0event |  -.5300509    .167397    -3.17   0.002    -.8587878   -.2013139
          L1event |  -.0018816   .2026481    -0.01   0.993    -.3998451     .396082
          L2event |   .2664154   .2414528     1.10   0.270    -.2077536    .7405844
          L3event |  -.0110278   .2511612    -0.04   0.965    -.5042622    .4822066
          L4event |   1.429649   .7515148     1.90   0.058    -.0461877    2.905486
          L5event |   1.477225   .6837042     2.16   0.031     .1345559    2.819895
          L6event |   .4332568   .9361461     0.46   0.644    -1.405162    2.271676
          L7event |   .5087222   .8213052     0.62   0.536     -1.10417    2.121615
        ln_ew_ges |   1.650218   1.072406     1.54   0.124    -.4557902    3.756225
         ew_biodt |   .7902138   .0329271    24.00   0.000      .725551    .8548767
        ew_dtmihi |  -.1665338   .0542449    -3.07   0.002    -.2730608   -.0600067
         ew_ledig |   .3830132   .0745097     5.14   0.000     .2366899    .5293365
       ew_married |   .6156742   .0733212     8.40   0.000     .4716849    .7596636
        wb_anteil |    -.54133   .0277962   -19.47   0.000    -.5959166   -.4867435
          wb_ausl |  -.0348043   .0125578    -2.77   0.006    -.0594655   -.0101431
         wb_18t24 |   -.056542   .0267672    -2.11   0.035    -.1091079    -.003976
         wb_25t34 |  -.0117404   .0144449    -0.81   0.417    -.0401076    .0166268
         wb_35t44 |  -.0065333   .0192077    -0.34   0.734    -.0442536     .031187
         wb_45t59 |   -.012029   .0192847    -0.62   0.533    -.0499007    .0258427
          avg_dur |   .0416426   .0224601     1.85   0.064    -.0024648      .08575
          hh_kids |  -.0901732   .0367571    -2.45   0.014    -.1623574    -.017989
mpreis_flats_rent |   .1036902    .020883     4.97   0.000     .0626798    .1447006
            _cons |  -1.293706   10.38581    -0.12   0.901    -21.68953    19.10211
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      43.42
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4435
Number of clusters (sb_new)  =        618         Root MSE        =     1.6190

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
   ln_street_dist |  -.7804117   .2217559    -3.52   0.000      -1.2159   -.3449238
          F7event |  -.0615857   .3193989    -0.19   0.847    -.6888264     .565655
          F6event |   .2532106   .2715356     0.93   0.351    -.2800355    .7864567
          F5event |  -.2425849   .2602925    -0.93   0.352    -.7537515    .2685816
          F4event |  -.2199498   .1683998    -1.31   0.192     -.550656    .1107565
          F3event |  -.0579995   .1575428    -0.37   0.713    -.3673846    .2513855
          F2event |   -.049704   .1322898    -0.38   0.707    -.3094969    .2100889
          L0event |  -.2936301   .1607175    -1.83   0.068    -.6092497    .0219895
          L1event |   .0719821   .2009964     0.36   0.720    -.3227378     .466702
          L2event |   .3690474    .226147     1.63   0.103    -.0750637    .8131584
          L3event |   .1112331   .2348214     0.47   0.636     -.349913    .5723791
          L4event |   .7209698    .619294     1.16   0.245    -.4952098    1.937149
          L5event |   1.843545   .7121437     2.59   0.010     .4450252    3.242064
          L6event |   .7385684    .797281     0.93   0.355    -.8271451    2.304282
          L7event |   .4127393   1.121021     0.37   0.713     -1.78874    2.614218
        ln_ew_ges |   1.517182   1.074078     1.41   0.158    -.5921094    3.626474
         ew_biodt |   .7614151   .0314393    24.22   0.000      .699674    .8231562
        ew_dtmihi |  -.1741854   .0518757    -3.36   0.001    -.2760598    -.072311
         ew_ledig |   .4308573   .0716867     6.01   0.000     .2900779    .5716367
       ew_married |   .6365164   .0697953     9.12   0.000     .4994513    .7735814
        wb_anteil |  -.5321212   .0239958   -22.18   0.000    -.5792445   -.4849978
          wb_ausl |   -.052919   .0174602    -3.03   0.003    -.0872075   -.0186304
         wb_18t24 |  -.0421214   .0256799    -1.64   0.101    -.0925519    .0083092
         wb_25t34 |  -.0148351   .0167379    -0.89   0.376    -.0477052     .018035
         wb_35t44 |  -.0038286   .0210864    -0.18   0.856    -.0452384    .0375812
         wb_45t59 |  -.0234675   .0196417    -1.19   0.233    -.0620401    .0151051
          avg_dur |   .0187427   .0222799     0.84   0.401    -.0250109    .0624964
          hh_kids |  -.1093901   .0353922    -3.09   0.002    -.1788938   -.0398863
mpreis_flats_rent |   .0166724   .0235323     0.71   0.479    -.0295406    .0628855
            _cons |   .1013546   10.02545     0.01   0.992    -19.58679    19.78949
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Log walking distance              -3.39***                         -3.43***             
                                        (0.25)                           (0.26)              
     Reassignment (#t-4#)                0.01                             -0.15              
                                        (0.17)                           (0.19)              
     Reassignment (#t-3#)                -0.10                            -0.10              
                                        (0.17)                           (0.20)              
     Reassignment (#t-2#)                0.02                             0.16               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)               -0.58**                          -0.66**             
                                        (0.21)                           (0.22)              
     Reassignment (#t+1#)               -0.63**                          -0.64**             
                                        (0.20)                           (0.23)              
     Reassignment (#t+2#)                -0.43                            -0.44              
                                        (0.23)                           (0.24)              
     R2                                  0.98                             0.97               
     N                                   4,666                            4,666              
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Controls                              X                                X                
     Election FE                                                           X                 
    ------------------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Log walking distance            2.61***                    2.62***          
                                           (0.25)                     (0.26)           
           Reassignment (#t-4#)             -0.23                      -0.08           
                                           (0.16)                     (0.17)           
           Reassignment (#t-3#)             0.04                       -0.07           
                                           (0.15)                     (0.20)           
           Reassignment (#t-2#)             -0.07                      -0.17           
                                           (0.12)                     (0.14)           
           Reassignment (#t+0#)             0.29                       0.23            
                                           (0.20)                     (0.23)           
           Reassignment (#t+1#)            0.70***                    0.69**           
                                           (0.20)                     (0.22)           
           Reassignment (#t+2#)            0.80***                    0.77**           
                                           (0.24)                     (0.26)           
           R2                               0.96                       0.95            
           N                                4,666                      4,666           
           Precinct FE                       X                          X              
           Election-District FE              X                                         
           Controls                           X                          X             
           Election FE                                                  X              
          ------------------------------------------------------------------------------


                        -------------------------------------------------
                                                Effect on total turnout 
                        -------------------------------------------------
                         Log walking distance          -0.78***         
                                                        (0.22)          
                         Reassignment (#t-4#)            -0.22          
                                                        (0.17)          
                         Reassignment (#t-3#)            -0.06          
                                                        (0.16)          
                         Reassignment (#t-2#)            -0.05          
                                                        (0.13)          
                         Reassignment (#t+0#)            -0.29          
                                                        (0.16)          
                         Reassignment (#t+1#)            0.07           
                                                        (0.20)          
                         Reassignment (#t+2#)            0.37           
                                                        (0.23)          
                         R2                              0.99           
                         N                               4,666          
                         Precinct FE                      X             
                         Election-District FE             X             
                         Controls                          X            
                         Election FE                                    
                        -------------------------------------------------

(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      43.19
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9876
                                                  Adj R-squared   =     0.9856
                                                  Within R-sq.    =     0.4491
Number of clusters (sb_new)  =        618         Root MSE        =     1.7944

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
   ln_street_dist |  -.8069275   .2390101    -3.38   0.001    -1.276299   -.3375556
          F7event |   .0129449   .3205699     0.04   0.968    -.6165954    .6424852
          F6event |   .1421461   .2711827     0.52   0.600     -.390407    .6746992
          F5event |    -.24852   .2729302    -0.91   0.363    -.7845047    .2874647
          F4event |   -.232143   .1702714    -1.36   0.173    -.5665247    .1022388
          F3event |   -.165681   .1673897    -0.99   0.323    -.4944036    .1630415
          F2event |  -.0093805   .1530822    -0.06   0.951    -.3100058    .2912448
          L0event |  -.4349993   .1670525    -2.60   0.009    -.7630597   -.1069389
          L1event |   .0536516   .2040015     0.26   0.793    -.3469698    .4542731
          L2event |    .328706   .2437291     1.35   0.178    -.1499332    .8073452
          L3event |  -.0243185   .2523148    -0.10   0.923    -.5198185    .4711814
          L4event |   1.440299   .7560865     1.90   0.057    -.0445156    2.925114
          L5event |   1.475648    .678837     2.17   0.030     .1425368    2.808759
          L6event |   .4638191   .9364979     0.50   0.621    -1.375291    2.302929
          L7event |   .5092405   .8391238     0.61   0.544    -1.138644    2.157125
        ln_ew_ges |   1.519349   1.067014     1.42   0.155    -.5760697    3.614768
         ew_biodt |   .7901359   .0329695    23.97   0.000     .7253898     .854882
        ew_dtmihi |   -.175733   .0539184    -3.26   0.001    -.2816187   -.0698472
         ew_ledig |   .3885608   .0767774     5.06   0.000      .237784    .5393375
       ew_married |   .6129874   .0748371     8.19   0.000     .4660211    .7599537
        wb_anteil |  -.5418096    .027918   -19.41   0.000    -.5966355   -.4869838
          wb_ausl |  -.0344104   .0124898    -2.76   0.006     -.058938   -.0098827
         wb_18t24 |  -.0545371   .0266569    -2.05   0.041    -.1068863   -.0021879
         wb_25t34 |  -.0087442   .0145132    -0.60   0.547    -.0372454     .019757
         wb_35t44 |  -.0080023   .0193951    -0.41   0.680    -.0460908    .0300862
         wb_45t59 |  -.0121767   .0192422    -0.63   0.527    -.0499648    .0256115
          avg_dur |   .0424358    .022645     1.87   0.061    -.0020348    .0869065
          hh_kids |   -.082451   .0359297    -2.29   0.022    -.1530103   -.0118917
mpreis_flats_rent |    .105276   .0209694     5.02   0.000     .0640959     .146456
            _cons |  -.8813874   10.31926    -0.09   0.932    -21.14651    19.38374
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    ------------------------------------------------------------------------------------------
                            Effect on polling place turnout  Effect on polling place turnout 
    ------------------------------------------------------------------------------------------
     Log walking distance              -3.39***                         -3.43***             
                                        (0.25)                           (0.26)              
     Reassignment (#t-4#)                0.01                             -0.15              
                                        (0.17)                           (0.19)              
     Reassignment (#t-3#)                -0.10                            -0.10              
                                        (0.17)                           (0.20)              
     Reassignment (#t-2#)                0.02                             0.16               
                                        (0.12)                           (0.14)              
     Reassignment (#t+0#)               -0.58**                          -0.66**             
                                        (0.21)                           (0.22)              
     Reassignment (#t+1#)               -0.63**                          -0.64**             
                                        (0.20)                           (0.23)              
     Reassignment (#t+2#)                -0.43                            -0.44              
                                        (0.23)                           (0.24)              
     R2                                  0.98                             0.97               
     N                                   4,666                            4,666              
     Precinct FE                          X                                X                 
     Election-District FE                 X                                                  
     Controls                              X                                X                
     Election FE                                                           X                 
    ------------------------------------------------------------------------------------------


          ------------------------------------------------------------------------------
                                  Effect on mail-in turnout  Effect on mail-in turnout 
          ------------------------------------------------------------------------------
           Log walking distance            2.61***                    2.62***          
                                           (0.25)                     (0.26)           
           Reassignment (#t-4#)             -0.23                      -0.08           
                                           (0.16)                     (0.17)           
           Reassignment (#t-3#)             0.04                       -0.07           
                                           (0.15)                     (0.20)           
           Reassignment (#t-2#)             -0.07                      -0.17           
                                           (0.12)                     (0.14)           
           Reassignment (#t+0#)             0.29                       0.23            
                                           (0.20)                     (0.23)           
           Reassignment (#t+1#)            0.70***                    0.69**           
                                           (0.20)                     (0.22)           
           Reassignment (#t+2#)            0.80***                    0.77**           
                                           (0.24)                     (0.26)           
           R2                               0.96                       0.95            
           N                                4,666                      4,666           
           Precinct FE                       X                          X              
           Election-District FE              X                                         
           Controls                           X                          X             
           Election FE                                                  X              
          ------------------------------------------------------------------------------


            --------------------------------------------------------------------------
                                    Effect on total turnout  Effect on total turnout 
            --------------------------------------------------------------------------
             Log walking distance          -0.78***                 -0.81***         
                                            (0.22)                   (0.24)          
             Reassignment (#t-4#)            -0.22                    -0.23          
                                            (0.17)                   (0.17)          
             Reassignment (#t-3#)            -0.06                    -0.17          
                                            (0.16)                   (0.17)          
             Reassignment (#t-2#)            -0.05                    -0.01          
                                            (0.13)                   (0.15)          
             Reassignment (#t+0#)            -0.29                   -0.43**         
                                            (0.16)                   (0.17)          
             Reassignment (#t+1#)            0.07                     0.05           
                                            (0.20)                   (0.20)          
             Reassignment (#t+2#)            0.37                     0.33           
                                            (0.23)                   (0.24)          
             R2                              0.99                     0.99           
             N                               4,666                    4,666          
             Precinct FE                      X                        X             
             Election-District FE             X                                      
             Controls                          X                        X            
             Election FE                                               X             
            --------------------------------------------------------------------------


. outreg, replay  ctitle("", "\multicolumn{2}{c}{\makecell{Turnout \\ at the Polling Place}}","","
> \multicolumn{2}{c}{\makecell{Turnout \\ by Mail}}","", "\multicolumn{2}{c}{\makecell{Total\\ Tur
> nout}}" \ ///
>                                                 "\cline{2-3} \cline{4-5} \cline{6-7}" \ "", (1),
>  (2), (3), (4), (5), (6)) store(tab) note("@{\extracolsep{4pt}}lcccccc@{}")             
warning: matrix in ctitles option has varying size rows:
   "", "\multicolumn{2}{c}{\makecell{Turnout \\ at the Polling Place}}","","\multicolumn{2}{c}{\ma
> kecell{Turnout \\ by Mail}}","", "\multicolumn{2}{c}{\makecell{Total\\ Turnout}}" \             
>                                     "\cline{2-3} \cline{4-5} \cline{6-7}" \ "", (1), (2), (3), (
> 4), (5), (6)

--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> -------------------------------------
                                      \multicolumn{2}{c}{\makecell{Turnout \ at the Polling Place}
> }             \multicolumn{2}{c}{\makecell{Turnout \ by Mail}}            \multicolumn{2}{c}{\ma
> kecell{Total\ Turnout}}            
\cline{2-3} \cline{4-5} \cline{6-7}                                                               
>                                                                                                 
>                                    
                                                                   (1)                            
>       (2)                            (3)                           (4)                         (
> 5)                          (6)    
--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> -------------------------------------
Log walking distance                                             -3.39***                         
>     -3.43***                       2.61***                       2.62***                     -0.
> 78***                     -0.81*** 
                                                                  (0.25)                          
>      (0.26)                        (0.25)                        (0.26)                       (0
> .22)                       (0.24)  
Reassignment (#t-4#)                                               0.01                           
>      -0.15                          -0.23                         -0.08                       -0
> .22                        -0.23   
                                                                  (0.17)                          
>      (0.19)                        (0.16)                        (0.17)                       (0
> .17)                       (0.17)  
Reassignment (#t-3#)                                              -0.10                           
>      -0.10                          0.04                          -0.07                       -0
> .06                        -0.17   
                                                                  (0.17)                          
>      (0.20)                        (0.15)                        (0.20)                       (0
> .16)                       (0.17)  
Reassignment (#t-2#)                                               0.02                           
>       0.16                          -0.07                         -0.17                       -0
> .05                        -0.01   
                                                                  (0.12)                          
>      (0.14)                        (0.12)                        (0.14)                       (0
> .13)                       (0.15)  
Reassignment (#t+0#)                                             -0.58**                          
>     -0.66**                         0.29                          0.23                        -0
> .29                       -0.43**  
                                                                  (0.21)                          
>      (0.22)                        (0.20)                        (0.23)                       (0
> .16)                       (0.17)  
Reassignment (#t+1#)                                             -0.63**                          
>     -0.64**                        0.70***                       0.69**                        0
> .07                         0.05   
                                                                  (0.20)                          
>      (0.23)                        (0.20)                        (0.22)                       (0
> .20)                       (0.20)  
Reassignment (#t+2#)                                              -0.43                           
>      -0.44                         0.80***                       0.77**                        0
> .37                         0.33   
                                                                  (0.23)                          
>      (0.24)                        (0.24)                        (0.26)                       (0
> .23)                       (0.24)  
R2                                                                 0.98                           
>       0.97                          0.96                          0.95                         0
> .99                         0.99   
N                                                                 4,666                           
>      4,666                          4,666                         4,666                       4,
> 666                        4,666   
Precinct FE                                                         X                             
>        X                             X                             X                            
> X                            X     
Election-District FE                                                X                             
>                                      X                                                          
> X                                  
Controls                                                            X                             
>        X                              X                             X                           
> X                            X     
Election FE                                                                                       
>        X                                                           X                            
>                              X     
--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> -------------------------------------
                                  @{\extracolsep{4pt}}lcccccc@{}


. outreg using "$tables/Table_1_ES_ctr_ln_street_dist", replay(tab) tex replace   fragment
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_1_
> ES_ctr_ln_street_dist.tex not found)
--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> -------------------------------------
                                      \multicolumn{2}{c}{\makecell{Turnout \ at the Polling Place}
> }             \multicolumn{2}{c}{\makecell{Turnout \ by Mail}}            \multicolumn{2}{c}{\ma
> kecell{Total\ Turnout}}            
\cline{2-3} \cline{4-5} \cline{6-7}                                                               
>                                                                                                 
>                                    
                                                                   (1)                            
>       (2)                            (3)                           (4)                         (
> 5)                          (6)    
--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> -------------------------------------
Log walking distance                                             -3.39***                         
>     -3.43***                       2.61***                       2.62***                     -0.
> 78***                     -0.81*** 
                                                                  (0.25)                          
>      (0.26)                        (0.25)                        (0.26)                       (0
> .22)                       (0.24)  
Reassignment (#t-4#)                                               0.01                           
>      -0.15                          -0.23                         -0.08                       -0
> .22                        -0.23   
                                                                  (0.17)                          
>      (0.19)                        (0.16)                        (0.17)                       (0
> .17)                       (0.17)  
Reassignment (#t-3#)                                              -0.10                           
>      -0.10                          0.04                          -0.07                       -0
> .06                        -0.17   
                                                                  (0.17)                          
>      (0.20)                        (0.15)                        (0.20)                       (0
> .16)                       (0.17)  
Reassignment (#t-2#)                                               0.02                           
>       0.16                          -0.07                         -0.17                       -0
> .05                        -0.01   
                                                                  (0.12)                          
>      (0.14)                        (0.12)                        (0.14)                       (0
> .13)                       (0.15)  
Reassignment (#t+0#)                                             -0.58**                          
>     -0.66**                         0.29                          0.23                        -0
> .29                       -0.43**  
                                                                  (0.21)                          
>      (0.22)                        (0.20)                        (0.23)                       (0
> .16)                       (0.17)  
Reassignment (#t+1#)                                             -0.63**                          
>     -0.64**                        0.70***                       0.69**                        0
> .07                         0.05   
                                                                  (0.20)                          
>      (0.23)                        (0.20)                        (0.22)                       (0
> .20)                       (0.20)  
Reassignment (#t+2#)                                              -0.43                           
>      -0.44                         0.80***                       0.77**                        0
> .37                         0.33   
                                                                  (0.23)                          
>      (0.24)                        (0.24)                        (0.26)                       (0
> .23)                       (0.24)  
R2                                                                 0.98                           
>       0.97                          0.96                          0.95                         0
> .99                         0.99   
N                                                                 4,666                           
>      4,666                          4,666                         4,666                       4,
> 666                        4,666   
Precinct FE                                                         X                             
>        X                             X                             X                            
> X                            X     
Election-District FE                                                X                             
>                                      X                                                          
> X                                  
Controls                                                            X                             
>        X                              X                             X                           
> X                            X     
Election FE                                                                                       
>        X                                                           X                            
>                              X     
--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> -------------------------------------
                                  @{\extracolsep{4pt}}lcccccc@{}


. 
. 
.         // auxillary tests of L1event-L0event for mail-in and in-person after absorbing log_stre
> et dist
.          estimates restore turnout_pos_req_fe_de
(results turnout_pos_req_fe_de are active now)

.          lincom L1event-L0event

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .410605   .1623707     2.53   0.012     .0917388    .7294711
------------------------------------------------------------------------------

.         di stritrim("Mail-in: tau1-tau0:`:di %12.2f `r(estimate)'', p=`:di %12.3f `r(p)''") // M
> ail-in: tau1-tau0: 0.41, p= 0.012
Mail-in: tau1-tau0: 0.41, p= 0.012

.          estimates restore turnout_urne_fe_de
(results turnout_urne_fe_de are active now)

.          lincom L1event-L0event

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0449928   .1699938    -0.26   0.791    -.3788294    .2888439
------------------------------------------------------------------------------

.         di stritrim("In-person: tau1-tau0:`:di %12.2f `r(estimate)'', p=`:di %12.3f `r(p)''") //
>  In-person: tau1-tau0: -0.04, p= 0.791
In-person: tau1-tau0: -0.04, p= 0.791

.         
.         
.  ** compute fraction of effect explained by distance
.         cap frame drop ctr_eq

.         cap frame drop bsl_eq

.         frame create bsl_eq str25 outcome str10 model str15 estimate coef_bsl   

.         frame create ctr_eq str25 outcome str10 model str15 estimate coef_ctr   

.         
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                 estimates restore `v'b_fe_de // bsl elec-district FE
  3.                 forvalues j = 0/2 {
  4.                         frame post bsl_eq ("`v'") ("D-E FE") ("L`j'event") (_b[L`j'event]) 
  5.                 }
  6.                 estimates restore `v'b_fe_e
  7.                 forvalues j = 0/2 {
  8.                         frame post bsl_eq ("`v'") ("E FE") ("L`j'event") (_b[L`j'event]) 
  9.                 }               
 10.                 estimates restore `v'_fe_de
 11.                 forvalues j = 0/2 {
 12.                         frame post ctr_eq ("`v'") ("D-E FE") ("L`j'event")  (_b[L`j'event]) 
 13.                 }               
 14.                 estimates restore `v'_fe_e
 15.                 forvalues j = 0/2 {
 16.                         frame post ctr_eq ("`v'") ("E FE") ("L`j'event")  (_b[L`j'event]) 
 17.                 }       
 18.         }
(results turnout_urneb_fe_de are active now)
(results turnout_urneb_fe_e are active now)
(results turnout_urne_fe_de are active now)
(results turnout_urne_fe_e are active now)
(results turnout_pos_reqb_fe_de are active now)
(results turnout_pos_reqb_fe_e are active now)
(results turnout_pos_req_fe_de are active now)
(results turnout_pos_req_fe_e are active now)
(results turnout_tot_reqb_fe_de are active now)
(results turnout_tot_reqb_fe_e are active now)
(results turnout_tot_req_fe_de are active now)
(results turnout_tot_req_fe_e are active now)

.         frame bsl_eq{
.                 frlink 1:1 outcome model estimate, frame(ctr_eq)
  (all observations in frame bsl_eq matched)
.                 frget coef_ctr, from(ctr_eq)
  (1 variable copied from linked frame)
.                 
.                 gen frac_dist = 1-(coef_ctr/coef_bsl)
.                 drop if outcome=="turnout_tot_req" & estimate!="L0event"        // keep only L0e
> vent for total
(4 observations deleted)
.                 bys outcome model: egen tot_frac_dist=mean(frac_dist)           // AVG frac expl
> ained by dist 
.                 sort model outcome  estimate
.                 list

     +---------------------------------------------------------------------------------+
  1. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     | turnout_pos_req | D-E FE |  L0event |  .6092887 |      1 |  .2895979 | .5246951 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                     .327724                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
  2. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     | turnout_pos_req | D-E FE |  L1event |  .9038026 |      2 |  .7002029 | .2252702 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                     .327724                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
  3. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     | turnout_pos_req | D-E FE |  L2event |  1.047491 |      3 |  .8032091 | .2332068 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                     .327724                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
  4. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     | turnout_tot_req | D-E FE |  L0event | -.3892706 |      7 | -.2936301 | .2456917 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .2456917                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
  5. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     |    turnout_urne | D-E FE |  L0event | -.9985597 |     13 | -.5832283 | .4159304 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3781718                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
  6. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     |    turnout_urne | D-E FE |  L1event |  -.892731 |     14 | -.6282211 |  .296293 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3781718                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
  7. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     |    turnout_urne | D-E FE |  L2event | -.7515249 |     15 |  -.434162 | .4222919 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3781718                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
  8. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     | turnout_pos_req |   E FE |  L0event |  .5381032 |      4 |    .22924 | .5739851 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3301216                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
  9. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     | turnout_pos_req |   E FE |  L1event |  .8706087 |      5 |  .6901577 |   .20727 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3301216                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
 10. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     | turnout_pos_req |   E FE |  L2event |  .9679545 |      6 |  .7655458 | .2091098 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3301216                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
 11. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     | turnout_tot_req |   E FE |  L0event | -.5300509 |     10 | -.4349993 | .1793253 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .1793253                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
 12. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     |    turnout_urne |   E FE |  L0event | -1.068154 |     16 | -.6642396 | .3781427 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3419756                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
 13. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     |    turnout_urne |   E FE |  L1event | -.8724909 |     17 | -.6365066 | .2704719 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3419756                                     |
     +---------------------------------------------------------------------------------+

     +---------------------------------------------------------------------------------+
 14. |         outcome |  model | estimate |  coef_bsl | ctr_eq |  coef_ctr | frac_d~t |
     |    turnout_urne |   E FE |  L2event | -.7015395 |     18 | -.4368401 | .3773121 |
     |---------------------------------------------------------------------------------|
     |                                    tot_fr~t                                     |
     |                                    .3419756                                     |
     +---------------------------------------------------------------------------------+
.                 
.                 levelsof outcome, local(outc)
`"turnout_pos_req"' `"turnout_tot_req"' `"turnout_urne"'
.                 foreach v of local outc {
  2.                         preserve
  3.                                 keep if outcome=="`v'"
  4.                                 duplicates drop model, force
  5.                                 sort model
  6.                                 local `v'_fe_de =`:di %9.2g tot_frac_dist[1]'
  7.                                 local `v'_fe_e =`:di %9.2g tot_frac_dist[2]'
  8.                         restore
  9.                 }
(8 observations deleted)

Duplicates in terms of model

(4 observations deleted)
(12 observations deleted)

Duplicates in terms of model

(0 observations are duplicates)
(8 observations deleted)

Duplicates in terms of model

(4 observations deleted)
.         }

.         
.         // Export Table: replay table from above and add fraction explained by distance 
.         outreg using "$tables/Table_1_ES_ctr_ln_street_dist", replay(tab) tex replace   fragment
>  ///
>                 addrow("\makecell[l]{Fraction of effect\\explained by distance}", ///
>                                 `turnout_urne_fe_de' , `turnout_urne_fe_e', `turnout_pos_req_fe_
> de' , ///
>                                 `turnout_pos_req_fe_e', `turnout_tot_req_fe_de' ,`turnout_tot_re
> q_fe_e')        

--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> ---------------------------------------------------------
                                                          \multicolumn{2}{c}{\makecell{Turnout \ a
> t the Polling Place}}             \multicolumn{2}{c}{\makecell{Turnout \ by Mail}}            \m
> ulticolumn{2}{c}{\makecell{Total\ Turnout}}            
\cline{2-3} \cline{4-5} \cline{6-7}                                                               
>                                                                                                 
>                                                        
                                                                                       (1)        
>                           (2)                            (3)                           (4)      
>                    (5)                          (6)    
--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> ---------------------------------------------------------
Log walking distance                                                                 -3.39***     
>                         -3.43***                       2.61***                       2.62***    
>                  -0.78***                     -0.81*** 
                                                                                      (0.25)      
>                          (0.26)                        (0.25)                        (0.26)     
>                   (0.22)                       (0.24)  
Reassignment (#t-4#)                                                                   0.01       
>                          -0.15                          -0.23                         -0.08     
>                   -0.22                        -0.23   
                                                                                      (0.17)      
>                          (0.19)                        (0.16)                        (0.17)     
>                   (0.17)                       (0.17)  
Reassignment (#t-3#)                                                                  -0.10       
>                          -0.10                          0.04                          -0.07     
>                   -0.06                        -0.17   
                                                                                      (0.17)      
>                          (0.20)                        (0.15)                        (0.20)     
>                   (0.16)                       (0.17)  
Reassignment (#t-2#)                                                                   0.02       
>                           0.16                          -0.07                         -0.17     
>                   -0.05                        -0.01   
                                                                                      (0.12)      
>                          (0.14)                        (0.12)                        (0.14)     
>                   (0.13)                       (0.15)  
Reassignment (#t+0#)                                                                 -0.58**      
>                         -0.66**                         0.29                          0.23      
>                   -0.29                       -0.43**  
                                                                                      (0.21)      
>                          (0.22)                        (0.20)                        (0.23)     
>                   (0.16)                       (0.17)  
Reassignment (#t+1#)                                                                 -0.63**      
>                         -0.64**                        0.70***                       0.69**     
>                    0.07                         0.05   
                                                                                      (0.20)      
>                          (0.23)                        (0.20)                        (0.22)     
>                   (0.20)                       (0.20)  
Reassignment (#t+2#)                                                                  -0.43       
>                          -0.44                         0.80***                       0.77**     
>                    0.37                         0.33   
                                                                                      (0.23)      
>                          (0.24)                        (0.24)                        (0.26)     
>                   (0.23)                       (0.24)  
R2                                                                                     0.98       
>                           0.97                          0.96                          0.95      
>                    0.99                         0.99   
N                                                                                     4,666       
>                          4,666                          4,666                         4,666     
>                   4,666                        4,666   
Precinct FE                                                                             X         
>                            X                             X                             X        
>                     X                            X     
Election-District FE                                                                    X         
>                                                          X                                      
>                     X                                  
Controls                                                                                X         
>                            X                              X                             X       
>                     X                            X     
Election FE                                                                                       
>                            X                                                           X        
>                                                  X     
\makecell[l]{Fraction of effect\explained by distance}                                 .38        
>                           .34                           .33                            .33      
>                    .25                          .18    
--------------------------------------------------------------------------------------------------
> ------------------------------------------------------------------------------------------------
> ---------------------------------------------------------
                                  @{\extracolsep{4pt}}lcccccc@{}


.         // clean .tex table for Overleaf 
.                 cleantex "$tables/Table_1_ES_ctr_ln_street_dist.tex" , nodisplay        replace

. 
.         
.         // auxillary regression with Z-SCORE distance for comparision w/ Cantoni (2020)
.         outreg, clear

.         cap drop zscore_*

.         zscore street_dist, stub(zscore_)
zscore_street_dist created with 0 missing values

.         reghdfe turnout_tot_req zscore_street_dist F* L*        $ctr    if smpl_trim==1         
> $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      43.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4425
Number of clusters (sb_new)  =        618         Root MSE        =     1.6204

                                     (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------------
                   |               Robust
   turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
zscore_street_dist |  -.3055276    .104649    -2.92   0.004     -.511039   -.1000161
           F7event |  -.0653857   .3198059    -0.20   0.838    -.6934256    .5626543
           F6event |   .2521289   .2715603     0.93   0.354    -.2811657    .7854234
           F5event |  -.2398729   .2601485    -0.92   0.357    -.7507567     .271011
           F4event |  -.2186006   .1682324    -1.30   0.194    -.5489781    .1117769
           F3event |  -.0546283   .1575671    -0.35   0.729    -.3640612    .2548047
           F2event |  -.0482214   .1322751    -0.36   0.716    -.3079855    .2115426
           L0event |  -.2846774   .1615239    -1.76   0.078    -.6018806    .0325259
           L1event |   .0802584   .2012876     0.40   0.690    -.3150335    .4755502
           L2event |   .3793722   .2258402     1.68   0.093    -.0641365    .8228808
           L3event |   .1323955   .2346752     0.56   0.573    -.3284635    .5932545
           L4event |   .7181517   .6172067     1.16   0.245    -.4939289    1.930232
           L5event |   1.861157   .7113834     2.62   0.009     .4641307    3.258183
           L6event |   .7498735   .7941119     0.94   0.345    -.8096163    2.309363
           L7event |   .4447057   1.121657     0.40   0.692    -1.758022    2.647434
         ln_ew_ges |   1.619326   1.086945     1.49   0.137    -.5152341    3.753886
          ew_biodt |   .7607103   .0315276    24.13   0.000     .6987958    .8226248
         ew_dtmihi |  -.1767517   .0518526    -3.41   0.001    -.2785807   -.0749227
          ew_ledig |   .4281479   .0731072     5.86   0.000     .2845788    .5717169
        ew_married |   .6335393   .0710106     8.92   0.000     .4940876     .772991
         wb_anteil |  -.5308853   .0240276   -22.09   0.000    -.5780711   -.4836996
           wb_ausl |  -.0529093    .017494    -3.02   0.003    -.0872643   -.0185543
          wb_18t24 |  -.0432045   .0257615    -1.68   0.094    -.0937953    .0073863
          wb_25t34 |  -.0149756   .0167841    -0.89   0.373    -.0479365    .0179852
          wb_35t44 |  -.0051609   .0211491    -0.24   0.807    -.0466938    .0363719
          wb_45t59 |  -.0240701   .0196868    -1.22   0.222    -.0627315    .0145912
           avg_dur |   .0184309   .0222954     0.83   0.409    -.0253532     .062215
           hh_kids |  -.1082126    .035486    -3.05   0.002    -.1779007   -.0385246
 mpreis_flats_rent |   .0155448   .0234932     0.66   0.508    -.0305915    .0616812
             _cons |  -.0492146   10.12244    -0.00   0.996    -19.92783     19.8294
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         outreg, $opt keep(zscore_street_dist)   
warning: no existing table found for merge or append

                         -----------------------------------------------
                                               Effect on total turnout 
                         -----------------------------------------------
                          zscore_street_dist           -0.31**         
                                                       (0.10)          
                          R2                            0.99           
                          N                             4,666          
                         -----------------------------------------------


.         reghdfe turnout_urne zscore_street_dist F* L*   $ctr    if smpl_trim==1         $wgt, ab
> sorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      24.31
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9753
                                                  Adj R-squared   =     0.9699
                                                  Within R-sq.    =     0.2682
Number of clusters (sb_new)  =        618         Root MSE        =     1.5937

                                     (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------------
                   |               Robust
      turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
zscore_street_dist |  -1.509349   .1192746   -12.65   0.000    -1.743583   -1.275116
           F7event |  -.0819455   .3357195    -0.24   0.807     -.741237     .577346
           F6event |   .0225309   .2906433     0.08   0.938    -.5482392     .593301
           F5event |   .2283798    .240548     0.95   0.343    -.2440123    .7007718
           F4event |    .016626   .1665454     0.10   0.921    -.3104385    .3436905
           F3event |  -.0841985   .1652373    -0.51   0.611    -.4086941    .2402971
           F2event |   .0333861   .1186674     0.28   0.779    -.1996548     .266427
           L0event |  -.4818542   .2141979    -2.25   0.025    -.9024995   -.0612088
           L1event |  -.5509405   .2020403    -2.73   0.007    -.9477105   -.1541705
           L2event |  -.3394892   .2252379    -1.51   0.132     -.781815    .1028367
           L3event |  -.2534067   .2461179    -1.03   0.304     -.736737    .2299236
           L4event |  -.5554128   .4224807    -1.31   0.189    -1.385087    .2742615
           L5event |  -.2859993   .5667318    -0.50   0.614    -1.398957    .8269578
           L6event |   1.361436   .6665815     2.04   0.042     .0523926     2.67048
           L7event |   .9673958   1.254457     0.77   0.441    -1.496128    3.430919
         ln_ew_ges |    -1.0782   .7878073    -1.37   0.172    -2.625308    .4689092
          ew_biodt |   .3684764   .0259789    14.18   0.000     .3174586    .4194943
         ew_dtmihi |   .0166865   .0492754     0.34   0.735    -.0800813    .1134543
          ew_ledig |   .2339446   .0514087     4.55   0.000     .1329874    .3349018
        ew_married |   .4056809   .0533826     7.60   0.000     .3008473    .5105144
         wb_anteil |  -.2837927   .0190951   -14.86   0.000    -.3212921   -.2462934
           wb_ausl |    .018474   .0148467     1.24   0.214    -.0106822    .0476301
          wb_18t24 |   -.013537   .0288132    -0.47   0.639    -.0701208    .0430468
          wb_25t34 |  -.0486807   .0176906    -2.75   0.006    -.0834217   -.0139397
          wb_35t44 |  -.0054209   .0206023    -0.26   0.793    -.0458801    .0350383
          wb_45t59 |   .0142726   .0208043     0.69   0.493    -.0265832    .0551284
           avg_dur |  -.0223037   .0202819    -1.10   0.272    -.0621336    .0175261
           hh_kids |  -.0176532   .0381124    -0.46   0.643    -.0924989    .0571925
 mpreis_flats_rent |   .0337534   .0236642     1.43   0.154    -.0127187    .0802255
             _cons |   12.92888   7.703686     1.68   0.094    -2.199747     28.0575
------------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         outreg, $opt keep(zscore_street_dist)

         --------------------------------------------------------------------------------
                               Effect on total turnout  Effect on polling place turnout 
         --------------------------------------------------------------------------------
          zscore_street_dist           -0.31**                     -1.51***             
                                       (0.10)                       (0.12)              
          R2                            0.99                         0.98               
          N                             4,666                        4,666              
         --------------------------------------------------------------------------------


. 
.         su street_dist [aw=wahlber_gesamt]

    Variable |     Obs      Weight        Mean   Std. dev.       Min        Max
-------------+-----------------------------------------------------------------
 street_dist |   4,944     7710778    .7117481   .3400581   .1561487   2.558443

.         di "One SD of Street Dist is equiv. to `:di %12.3f `r(sd)'' km and `:di %12.3f 0.621371 
> *`r(sd)'' miles"
One SD of Street Dist is equiv. to        0.340 km and        0.211 miles

. 
. 
. 
. ********************************************************************************
. *       Robustness to clustering: Baseline ES, treat=100%, smpl_trim, (Table C2)
. *       Note for bootstrapping:
. *               - reghdfe does not work with bootest + boottype(wild)
. *               - areg does not work for TWOWAY clustering (does not throw error BUT OLS estimat
> es CHANGE)
. ********************************************************************************
. 
.         * TABLE C2. Robustness to Clustering at Different Levels                
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen     F`l'event = K==-`l'
  3.                 lab var F`l'event "Reassignment (#t-`l'#)"
  4.         }       

.         forvalues l = 0/7 {
  2.                 gen     L`l'event = K==`l'
  3.                 lab var L`l'event "Reassignment (#t+`l'#)"
  4.         }               

.                 drop    F1event // drop reference period

.                 
.                 
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                 outreg, clear
  3.                 
.                 *----- ELECTION-District FE     
.                 // (1) Baseline Event study (weighted) -- ELECTION-DISTRICT FE
.                  reghdfe `v' F* L*                                      $ctr    if smpl_trim==1 
>         $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new) 
  4.                  outreg,  $opt  keep($order) addrow(Precinct FE, X \ Election-District FE, X 
> \ Election-District FE,  \ Cluster Precinct, X \ Number of Clusters, 618)
  5.          
.                 // (2) TW cluster(District-Election + precinct) -- ELECTION-DISTRICT FE
.                  reghdfe `v' F* L*                                      $ctr    if smpl_trim==1 
>         $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new i.stadtbez#i.wahl_id)
  6.                  outreg,  $opt  keep($order) addrow(Precinct FE, X \ Election-District FE, X 
> \ TW Cluster District-Election + Precinct, X \ Number of Clusters, 200+618)
  7.                 
.                 // (3) Wild Bootstrap cluster(Precinct) -- ELECTION-DISTRICT FE
.                  areg `v' F* L* i.wahl_id#i.stadtbez $ctr       if smpl_trim==1         $wgt, ab
> sorb(sb_new) cluster(sb_new)
  8.                 bexp
  9.                  outreg,  keep($order) stats(b se) brackets("","" \ [,]) nocons noleg nostars
>  summstat(r2 \ N )  statfont(fs32 fnew1) sdec(2\3) varlabels merge ///
>                         addrow(Precinct FE, X \ Election-District FE, X \ WRC Precinct, X \ Numb
> er of Clusters, 618)    
 10.                 
.                 // (4) Wild Boostrap cluster(District) -- ELECTION-DISTRICT FE
.                  areg `v' F* L* i.wahl_id#i.stadtbez $ctr       if smpl_trim==1         $wgt, ab
> sorb(sb_new) cluster(stadtbez)
 11.                 bexp
 12.                  outreg,  keep($order) stats(b se) brackets("","" \ [,]) nocons noleg nostars
>  summstat(r2 \ N )  statfont(fs32 fnew1) sdec(2\3) varlabels merge ///
>                         addrow(Precinct FE, X \ Election-District FE, X \ WRC District, X \ Numb
> er of Clusters, 25)     
 13.                                 
.                 *----- ELECTION FE
.                 // (5) Wild Boostrap cluster(District) -- ONLY ELECTION FE
.                  areg `v' F* L* i.wahl_id       $ctr    if smpl_trim==1         $wgt, absorb(sb_
> new) cluster(stadtbez)
 14.                 bexp
 15.                  outreg,  keep($order) stats(b se) brackets("","" \ [,]) nocons noleg nostars
>  summstat(r2 \ N )  statfont(fs32 fnew1) sdec(2\3) varlabels merge ///
>                         addrow(Precinct FE, X \ Election FE, X  \ WRC District, X \ Number of Cl
> usters, 25)     
 16.                         
.                 
.                 *save
.                  outreg, replay replace store(`v')
 17.         }
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      17.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9658
                                                  Within R-sq.    =     0.1691
Number of clusters (sb_new)  =        618         Root MSE        =     1.6979

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3609916    -0.46   0.646    -.8749677    .5428746
          F6event |   .0365542   .3229698     0.11   0.910    -.5976991    .6708075
          F5event |   .2427259   .2553086     0.95   0.342    -.2586533    .7441051
          F4event |   .0132213   .1747424     0.08   0.940    -.3299406    .3563833
          F3event |  -.0565081    .169943    -0.33   0.740    -.3902449    .2772287
          F2event |    .006289   .1214372     0.05   0.959    -.2321913    .2447694
          L0event |  -.9985596   .2347416    -4.25   0.000    -1.459549   -.5375702
          L1event |   -.892731   .2344548    -3.81   0.000    -1.353157   -.4323048
          L2event |  -.7515249   .2578216    -2.91   0.004    -1.257839   -.2452106
          L3event |  -.2931705   .2663112    -1.10   0.271    -.8161567    .2298157
          L4event |  -.8782313   .4731179    -1.86   0.064    -1.807348    .0508853
          L5event |  -.5487131   .6172865    -0.89   0.374     -1.76095    .6635241
          L6event |    1.07717   .7208125     1.49   0.136    -.3383731    2.492714
          L7event |   .9427971   1.187553     0.79   0.428    -1.389339    3.274933
        ln_ew_ges |  -.7878053   .9548651    -0.83   0.410    -2.662985    1.087374
         ew_biodt |    .373603   .0281093    13.29   0.000     .3184015    .4288045
        ew_dtmihi |   .0630129   .0513196     1.23   0.220    -.0377693    .1637951
         ew_ledig |   .2127399   .0574696     3.70   0.000     .0998801    .3255996
       ew_married |   .4312672   .0590852     7.30   0.000     .3152348    .5472997
        wb_anteil |  -.2895078   .0204524   -14.16   0.000    -.3296726   -.2493431
          wb_ausl |   .0153767   .0162706     0.95   0.345    -.0165758    .0473293
         wb_18t24 |  -.0164981   .0295896    -0.56   0.577    -.0746067    .0416105
         wb_25t34 |  -.0724461   .0193717    -3.74   0.000    -.1104886   -.0344037
         wb_35t44 |    .006101   .0230068     0.27   0.791    -.0390802    .0512822
         wb_45t59 |   .0140084   .0220328     0.64   0.525      -.02926    .0572768
          avg_dur |  -.0288813   .0210954    -1.37   0.171    -.0703088    .0125462
          hh_kids |  -.0506236   .0408723    -1.24   0.216    -.1308893    .0296421
mpreis_flats_rent |   .0303932   .0255105     1.19   0.234    -.0197047    .0804911
            _cons |   11.36038   9.043266     1.26   0.210    -6.398933    29.11969
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: matrix in addrows option has varying size rows:
   `"R2 "',`"0.97"' \ `"N"',`"4,666"'\Precinct FE, X \ Election-District FE, X \ Election-District
>  FE, \ Cluster Precinct, X \ Number of Clusters, 618
warning: no existing table found for merge or append

                    ---------------------------------------------------------
                                            Effect on polling place turnout 
                    ---------------------------------------------------------
                     Reassignment (#t-4#)                0.01               
                                                        (0.17)              
                     Reassignment (#t-3#)                -0.06              
                                                        (0.17)              
                     Reassignment (#t-2#)                0.01               
                                                        (0.12)              
                     Reassignment (#t+0#)              -1.00***             
                                                        (0.23)              
                     Reassignment (#t+1#)              -0.89***             
                                                        (0.23)              
                     Reassignment (#t+2#)               -0.75**             
                                                        (0.26)              
                     R2                                  0.97               
                     N                                   4,666              
                     Precinct FE                          X                 
                     Election-District FE                 X                 
                     Election-District FE                                   
                     Cluster Precinct                     X                 
                     Number of Clusters                   618               
                    ---------------------------------------------------------

(MWFE estimator converged in 8 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller appl
> ied.

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    199) =      17.93
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9658
Number of clusters (sb_new)  =        618         Within R-sq.    =     0.1691
Number of clusters (stadtbez#wahl_id) =        200Root MSE        =     1.6982

                   (Std. err. adjusted for 200 clusters in sb_new stadtbez#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3329527    -0.50   0.619    -.8226148    .4905216
          F6event |   .0365542   .3122051     0.12   0.907    -.5791007    .6522091
          F5event |   .2427259   .2644016     0.92   0.360    -.2786625    .7641142
          F4event |   .0132213    .186619     0.07   0.944    -.3547833     .381226
          F3event |  -.0565081   .1870802    -0.30   0.763    -.4254221     .312406
          F2event |    .006289   .1387406     0.05   0.964    -.2673015    .2798795
          L0event |  -.9985596   .2571042    -3.88   0.000    -1.505558   -.4915613
          L1event |   -.892731   .2584744    -3.45   0.001    -1.402431   -.3830307
          L2event |  -.7515249   .2732859    -2.75   0.007    -1.290433   -.2126169
          L3event |  -.2931705   .2923025    -1.00   0.317    -.8695783    .2832373
          L4event |  -.8782313    .436798    -2.01   0.046    -1.739578   -.0168847
          L5event |  -.5487131   .4615026    -1.19   0.236    -1.458776    .3613499
          L6event |    1.07717   .7407633     1.45   0.147    -.3835827    2.537923
          L7event |   .9427971   1.037366     0.91   0.365    -1.102845    2.988439
        ln_ew_ges |  -.7878053   .9604294    -0.82   0.413     -2.68173     1.10612
         ew_biodt |    .373603   .0295902    12.63   0.000     .3152525    .4319535
        ew_dtmihi |   .0630129   .0515406     1.22   0.223    -.0386229    .1646488
         ew_ledig |   .2127399   .0583267     3.65   0.000     .0977221    .3277577
       ew_married |   .4312672   .0585168     7.37   0.000     .3158746    .5466599
        wb_anteil |  -.2895078   .0240713   -12.03   0.000    -.3369754   -.2420403
          wb_ausl |   .0153767    .018102     0.85   0.397    -.0203196    .0510731
         wb_18t24 |  -.0164981   .0302587    -0.55   0.586    -.0761669    .0431707
         wb_25t34 |  -.0724461   .0206594    -3.51   0.001    -.1131856   -.0317067
         wb_35t44 |    .006101   .0242737     0.25   0.802    -.0417657    .0539677
         wb_45t59 |   .0140084   .0196011     0.71   0.476    -.0246441     .052661
          avg_dur |  -.0288813   .0217642    -1.33   0.186    -.0717993    .0140367
          hh_kids |  -.0506236   .0396106    -1.28   0.203     -.128734    .0274868
mpreis_flats_rent |   .0303932   .0259068     1.17   0.242    -.0206939    .0814803
            _cons |   11.36038   8.814536     1.29   0.199    -6.021503    28.74226
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200         200           0    *|
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.17)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.17)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.12)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.23)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.23)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.26)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                        X                 
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.19)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.19)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.14)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.26)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.26)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.27)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                    200+618             
            TW Cluster District-Election + Precinct                 X                 
           ----------------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)
note: 8.wahl_id#2.stadtbez omitted because of collinearity.
note: 8.wahl_id#3.stadtbez omitted because of collinearity.
note: 8.wahl_id#4.stadtbez omitted because of collinearity.
note: 8.wahl_id#5.stadtbez omitted because of collinearity.
note: 8.wahl_id#6.stadtbez omitted because of collinearity.
note: 8.wahl_id#7.stadtbez omitted because of collinearity.
note: 8.wahl_id#8.stadtbez omitted because of collinearity.
note: 8.wahl_id#9.stadtbez omitted because of collinearity.
note: 8.wahl_id#10.stadtbez omitted because of collinearity.
note: 8.wahl_id#11.stadtbez omitted because of collinearity.
note: 8.wahl_id#12.stadtbez omitted because of collinearity.
note: 8.wahl_id#13.stadtbez omitted because of collinearity.
note: 8.wahl_id#14.stadtbez omitted because of collinearity.
note: 8.wahl_id#15.stadtbez omitted because of collinearity.
note: 8.wahl_id#16.stadtbez omitted because of collinearity.
note: 8.wahl_id#17.stadtbez omitted because of collinearity.
note: 8.wahl_id#18.stadtbez omitted because of collinearity.
note: 8.wahl_id#19.stadtbez omitted because of collinearity.
note: 8.wahl_id#20.stadtbez omitted because of collinearity.
note: 8.wahl_id#21.stadtbez omitted because of collinearity.
note: 8.wahl_id#22.stadtbez omitted because of collinearity.
note: 8.wahl_id#23.stadtbez omitted because of collinearity.
note: 8.wahl_id#24.stadtbez omitted because of collinearity.
note: 8.wahl_id#25.stadtbez omitted because of collinearity.

Linear regression, absorbing indicators             Number of obs     =  4,666
Absorbed variable: sb_new                           No. of categories =    618
                                                    F(203, 617)       = 796.89
                                                    Prob > F          = 0.0000
                                                    R-squared         = 0.9720
                                                    Adj R-squared     = 0.9660
                                                    Root MSE          = 1.6926

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466    .387831    -0.43   0.669    -.9276754    .5955823
          F6event |   .0365542   .3469824     0.11   0.916    -.6448554    .7179638
          F5event |   .2427259   .2742906     0.88   0.377    -.2959305    .7813822
          F4event |   .0132213   .1877344     0.07   0.944    -.3554545    .3818971
          F3event |  -.0565081   .1825781    -0.31   0.757    -.4150579    .3020418
          F2event |    .006289    .130466     0.05   0.962    -.2499221    .2625002
          L0event |  -.9985596   .2521945    -3.96   0.000    -1.493823    -.503296
          L1event |   -.892731   .2518863    -3.54   0.000    -1.387389   -.3980725
          L2event |  -.7515249   .2769905    -2.71   0.007    -1.295483   -.2075665
          L3event |  -.2931705   .2861112    -1.02   0.306    -.8550403    .2686993
          L4event |  -.8782313   .5082938    -1.73   0.085    -1.876427    .1199644
          L5event |  -.5487131   .6631813    -0.83   0.408    -1.851079     .753653
          L6event |    1.07717   .7744044     1.39   0.165    -.4436177    2.597958
          L7event |   .9427971   1.275847     0.74   0.460    -1.562732    3.448326
                  |
 wahl_id#stadtbez |
        LTW13# 2  |   9.684951   .8206501    11.80   0.000     8.073344    11.29656
        LTW13# 3  |   7.399244   .6931184    10.68   0.000     6.038087    8.760401
        LTW13# 4  |   8.627009   .7885652    10.94   0.000     7.078412    10.17561
        LTW13# 5  |   10.45435   .7124418    14.67   0.000     9.055249    11.85346
        LTW13# 6  |   9.432198   .8581308    10.99   0.000     7.746987    11.11741
        LTW13# 7  |    9.63098   .8030739    11.99   0.000      8.05389    11.20807
        LTW13# 8  |   9.376405   1.325998     7.07   0.000     6.772388    11.98042
        LTW13# 9  |   10.48862      .7237    14.49   0.000     9.067411    11.90984
        LTW13#10  |   12.28635   .7256247    16.93   0.000     10.86136    13.71135
        LTW13#11  |   9.501168   .7162456    13.27   0.000     8.094594    10.90774
        LTW13#12  |   9.269689   .5992002    15.47   0.000      8.09297    10.44641
        LTW13#13  |    12.4975   .5689223    21.97   0.000     11.38024    13.61476
        LTW13#14  |   9.835615   .7825779    12.57   0.000     8.298776    11.37245
        LTW13#15  |   12.46628   .5619335    22.18   0.000     11.36274    13.56981
        LTW13#16  |   10.79237   .5947263    18.15   0.000     9.624435     11.9603
        LTW13#17  |   10.09305   .6914631    14.60   0.000     8.735142    11.45095
        LTW13#18  |   8.982139   .6979001    12.87   0.000     7.611591    10.35269
        LTW13#19  |   11.36311   .5878016    19.33   0.000     10.20878    12.51745
        LTW13#20  |   11.27723   .5530921    20.39   0.000     10.19106     12.3634
        LTW13#21  |   11.93798   .6238166    19.14   0.000     10.71292    13.16304
        LTW13#22  |   14.46903   .7976897    18.14   0.000     12.90252    16.03555
        LTW13#23  |   13.56508   1.007801    13.46   0.000     11.58594    15.54422
        LTW13#24  |   12.13182   .7606438    15.95   0.000     10.63805    13.62558
        LTW13#25  |   10.69362   .6285128    17.01   0.000     9.459341    11.92791
        BTW13# 1  |   5.179102   .4562534    11.35   0.000     4.283105      6.0751
        BTW13# 2  |   14.25267   .8410415    16.95   0.000     12.60102    15.90433
        BTW13# 3  |   12.68875   .7127245    17.80   0.000     11.28909    14.08841
        BTW13# 4  |   13.95461   .7634499    18.28   0.000     12.45533    15.45389
        BTW13# 5  |   14.92427    .679999    21.95   0.000     13.58887    16.25966
        BTW13# 6  |   13.96293   .8637691    16.17   0.000     12.26665    15.65922
        BTW13# 7  |   14.29354    .834305    17.13   0.000     12.65512    15.93196
        BTW13# 8  |   14.21507   1.289446    11.02   0.000     11.68284    16.74731
        BTW13# 9  |   15.47349   .7157378    21.62   0.000     14.06792    16.87907
        BTW13#10  |   17.72355   .6979422    25.39   0.000     16.35292    19.09418
        BTW13#11  |   15.46149   .7476878    20.68   0.000     13.99317    16.92981
        BTW13#12  |   14.45336   .5814037    24.86   0.000     13.31159    15.59513
        BTW13#13  |   17.60729   .5781989    30.45   0.000     16.47181    18.74276
        BTW13#14  |   15.26834   .6329506    24.12   0.000     14.02534    16.51134
        BTW13#15  |   17.47129   .4851232    36.01   0.000     16.51859    18.42398
        BTW13#16  |   16.16533   .5416035    29.85   0.000     15.10172    17.22894
        BTW13#17  |   15.58457   .6838332    22.79   0.000     14.24165     16.9275
        BTW13#18  |   13.53935    .665616    20.34   0.000      12.2322    14.84649
        BTW13#19  |   16.44375   .5822396    28.24   0.000     15.30034    17.58717
        BTW13#20  |   15.92281   .5551204    28.68   0.000     14.83266    17.01297
        BTW13#21  |   16.18185   .6532983    24.77   0.000     14.89889    17.46481
        BTW13#22  |   19.42762   .9016701    21.55   0.000      17.6569    21.19833
        BTW13#23  |   17.75538   1.149922    15.44   0.000     15.49715    20.01362
        BTW13#24  |   17.16014   .6718404    25.54   0.000     15.84077    18.47952
        BTW13#25  |    15.5023   .5638725    27.49   0.000     14.39496    16.60964
        KOW14# 1  |  -6.461544    .409932   -15.76   0.000    -7.266575   -5.656513
        KOW14# 2  |   1.504225   .7183267     2.09   0.037     .0935635    2.914887
        KOW14# 3  |  -.4507358   .5300143    -0.85   0.395    -1.491587    .5901149
        KOW14# 4  |   1.543643   .7046134     2.19   0.029     .1599117    2.927374
        KOW14# 5  |   1.856712   .5417505     3.43   0.001     .7928137     2.92061
        KOW14# 6  |   .1582679   .6627403     0.24   0.811    -1.143232    1.459768
        KOW14# 7  |   .4773112   .7218064     0.66   0.509    -.9401839    1.894806
        KOW14# 8  |   .3824901   1.073134     0.36   0.722    -1.724947    2.489928
        KOW14# 9  |   2.560967   .6036983     4.24   0.000     1.375414    3.746519
        KOW14#10  |   2.445451   .6037839     4.05   0.000     1.259731    3.631172
        KOW14#11  |   .9499184   .5414099     1.75   0.080    -.1133111    2.013148
        KOW14#12  |   .6485342   .4862626     1.33   0.183    -.3063962    1.603465
        KOW14#13  |   3.044832   .4831906     6.30   0.000     2.095935     3.99373
        KOW14#14  |  -.2173867   .4857845    -0.45   0.655    -1.171378    .7366047
        KOW14#15  |   .4702327   .5157178     0.91   0.362    -.5425423    1.483008
        KOW14#16  |    .664751   .4290283     1.55   0.122    -.1777818    1.507284
        KOW14#17  |   .7206238    .467052     1.54   0.123    -.1965806    1.637828
        KOW14#18  |   .6474164   .5882779     1.10   0.272    -.5078534    1.802686
        KOW14#19  |   2.060617   .4257385     4.84   0.000     1.224545    2.896689
        KOW14#20  |   .5931655    .496884     1.19   0.233    -.3826234    1.568954
        KOW14#21  |   2.544402   .5699225     4.46   0.000     1.425179    3.663626
        KOW14#22  |   2.906699   .7046458     4.13   0.000     1.522904    4.290494
        KOW14#23  |   3.058166   .8857855     3.45   0.001     1.318646    4.797686
        KOW14#24  |   .3911893   .4901056     0.80   0.425    -.5712881    1.353667
        KOW14#25  |   1.100464   .4286077     2.57   0.010     .2587573    1.942171
        EUW14# 1  |  -8.304891   .5062171   -16.41   0.000    -9.299009   -7.310774
        EUW14# 2  |   .5008454   .7048153     0.71   0.478    -.8832823    1.884973
        EUW14# 3  |  -1.044356   .5782514    -1.81   0.071    -2.179936    .0912232
        EUW14# 4  |   -.086198   .6959476    -0.12   0.901    -1.452911    1.280515
        EUW14# 5  |  -.1612736   .6340197    -0.25   0.799    -1.406372    1.083825
        EUW14# 6  |   -1.83557   .6945352    -2.64   0.008     -3.19951   -.4716306
        EUW14# 7  |  -2.152752    .815502    -2.64   0.009    -3.754248   -.5512562
        EUW14# 8  |  -2.078354   1.148043    -1.81   0.071      -4.3329     .176192
        EUW14# 9  |    .113158   .6241042     0.18   0.856    -1.112468    1.338784
        EUW14#10  |  -.9635493   .7023799    -1.37   0.171    -2.342894    .4157959
        EUW14#11  |  -1.141868   .7808425    -1.46   0.144    -2.675299    .3915634
        EUW14#12  |  -.8399715   .5684913    -1.48   0.140    -1.956384     .276441
        EUW14#13  |   .4052541   .5836931     0.69   0.488    -.7410119     1.55152
        EUW14#14  |  -2.063222   .5938399    -3.47   0.001    -3.229415     -.89703
        EUW14#15  |  -2.504358   .5623223    -4.45   0.000    -3.608655    -1.40006
        EUW14#16  |  -2.177233    .529045    -4.12   0.000     -3.21618   -1.138286
        EUW14#17  |  -1.732244   .6571652    -2.64   0.009    -3.022796   -.4416923
        EUW14#18  |  -1.028267   .6774851    -1.52   0.130    -2.358723    .3021894
        EUW14#19  |  -.4956762   .5511783    -0.90   0.369    -1.578089    .5867367
        EUW14#20  |  -1.447591    .761475    -1.90   0.058    -2.942988    .0478056
        EUW14#21  |  -.1012352    .623612    -0.16   0.871    -1.325895    1.123424
        EUW14#22  |  -.6242371   .6062488    -1.03   0.304    -1.814798    .5663241
        EUW14#23  |  -1.869141   .8778792    -2.13   0.034    -3.593134   -.1451472
        EUW14#24  |  -2.983598   .5808326    -5.14   0.000    -4.124247    -1.84295
        EUW14#25  |  -1.174533   .5645741    -2.08   0.038    -2.283252   -.0658129
        BTW17# 1  |   6.623327   .7422561     8.92   0.000     5.165672    8.080981
        BTW17# 2  |   16.12564    .900073    17.92   0.000     14.35806    17.89322
        BTW17# 3  |   15.36253    .672127    22.86   0.000     14.04259    16.68246
        BTW17# 4  |   16.91978   .7883321    21.46   0.000     15.37164    18.46792
        BTW17# 5  |     16.596   .8143238    20.38   0.000     14.99682    18.19519
        BTW17# 6  |   16.88396   .6804425    24.81   0.000     15.54769    18.22022
        BTW17# 7  |   18.39806   .7826957    23.51   0.000     16.86099    19.93513
        BTW17# 8  |   17.21364   1.114332    15.45   0.000      15.0253    19.40198
        BTW17# 9  |    18.3968   .5818028    31.62   0.000     17.25425    19.53936
        BTW17#10  |   21.03952   .5832241    36.07   0.000     19.89417    22.18486
        BTW17#11  |   19.63813   .7313705    26.85   0.000     18.20185    21.07441
        BTW17#12  |   17.49707   .7994645    21.89   0.000     15.92707    19.06707
        BTW17#13  |   19.03479   .6541426    29.10   0.000     17.75018    20.31941
        BTW17#14  |   17.46705   .8238974    21.20   0.000     15.84907    19.08503
        BTW17#15  |   21.29595   .6061741    35.13   0.000     20.10554    22.48636
        BTW17#16  |   18.52523   .5464449    33.90   0.000     17.45212    19.59835
        BTW17#17  |   17.53169    .856488    20.47   0.000      15.8497    19.21367
        BTW17#18  |    16.2884   .6880921    23.67   0.000     14.93711    17.63968
        BTW17#19  |   18.80557   .5664656    33.20   0.000     17.69314    19.91801
        BTW17#20  |   19.37811   .5145989    37.66   0.000     18.36754    20.38869
        BTW17#21  |   19.78952   .5998241    32.99   0.000     18.61157    20.96746
        BTW17#22  |   23.04139   .8319312    27.70   0.000     21.40763    24.67515
        BTW17#23  |   20.17909   .8768762    23.01   0.000     18.45707    21.90112
        BTW17#24  |   20.61607   .5729814    35.98   0.000     19.49084     21.7413
        BTW17#25  |   18.61648   .6307325    29.52   0.000     17.37784    19.85512
        LTW18# 1  |   4.247703   .7554266     5.62   0.000     2.764184    5.731222
        LTW18# 2  |   13.65979   .7068876    19.32   0.000     12.27159    15.04798
        LTW18# 3  |   13.41553   .5678777    23.62   0.000     12.30033    14.53074
        LTW18# 4  |   14.80755   .7654685    19.34   0.000     13.30431    16.31079
        LTW18# 5  |   13.22889   1.073712    12.32   0.000     11.12031    15.33746
        LTW18# 6  |   14.00326   .6362719    22.01   0.000     12.75373    15.25278
        LTW18# 7  |   15.07874    .779604    19.34   0.000     13.54774    16.60974
        LTW18# 8  |   14.28719    1.22786    11.64   0.000      11.8759    16.69848
        LTW18# 9  |   15.14662   .5772178    26.24   0.000     14.01307    16.28017
        LTW18#10  |   16.74482   .6901971    24.26   0.000      15.3894    18.10024
        LTW18#11  |   15.26507   .6482923    23.55   0.000     13.99194     16.5382
        LTW18#12  |   14.70073   .6713814    21.90   0.000     13.38226     16.0192
        LTW18#13  |   16.85409   .6827209    24.69   0.000     15.51335    18.19483
        LTW18#14  |   13.40328    .767492    17.46   0.000     11.89607    14.91049
        LTW18#15  |   18.17208   .6073152    29.92   0.000     16.97943    19.36474
        LTW18#16  |      15.54    .606346    25.63   0.000     14.34925    16.73075
        LTW18#17  |   14.77057   .7251733    20.37   0.000     13.34646    16.19468
        LTW18#18  |   15.48923   .6347929    24.40   0.000     14.24261    16.73585
        LTW18#19  |   16.45795   .6097502    26.99   0.000     15.26051    17.65539
        LTW18#20  |   15.57061   .6193693    25.14   0.000     14.35428    16.78694
        LTW18#21  |   17.28231   .6086778    28.39   0.000     16.08698    18.47764
        LTW18#22  |   19.43456   .8091632    24.02   0.000     17.84551    21.02361
        LTW18#23  |   17.69707   .8923116    19.83   0.000     15.94474    19.44941
        LTW18#24  |   16.08676   .7514187    21.41   0.000     14.61111    17.56241
        LTW18#25  |   14.83052   .7159304    20.72   0.000     13.42456    16.23647
        EUW19# 1  |  -.5317836   .7348861    -0.72   0.470    -1.974965    .9113976
        EUW19# 2  |   8.878702   .6251918    14.20   0.000      7.65094    10.10646
        EUW19# 3  |   8.957982   .4734631    18.92   0.000     8.028188    9.887777
        EUW19# 4  |   9.794111    .650635    15.05   0.000     8.516384    11.07184
        EUW19# 5  |   8.607378   .7654056    11.25   0.000     7.104262    10.11049
        EUW19# 6  |   9.152918   .6058159    15.11   0.000     7.963207    10.34263
        EUW19# 7  |   8.877967   .7508378    11.82   0.000      7.40346    10.35247
        EUW19# 8  |   8.449083   1.026088     8.23   0.000     6.434035    10.46413
        EUW19# 9  |   8.236168   .4824845    17.07   0.000     7.288657    9.183679
        EUW19#10  |   9.155411   .6866609    13.33   0.000     7.806935    10.50389
        EUW19#11  |   8.695064   .6070288    14.32   0.000     7.502971    9.887157
        EUW19#12  |   8.757942   .5476009    15.99   0.000     7.682555     9.83333
        EUW19#13  |   11.07401   .5255006    21.07   0.000     10.04202    12.10599
        EUW19#14  |    8.12026    .743327    10.92   0.000     6.660503    9.580018
        EUW19#15  |   10.76622   .5173977    20.81   0.000     9.750144    11.78229
        EUW19#16  |    8.68917   .4724654    18.39   0.000     7.761335    9.617006
        EUW19#17  |   8.225204   .5500667    14.95   0.000     7.144974    9.305433
        EUW19#18  |   8.457362   .5580222    15.16   0.000     7.361509    9.553215
        EUW19#19  |   9.496975   .5033351    18.87   0.000     8.508517    10.48543
        EUW19#20  |   9.247748   .7125856    12.98   0.000     7.848361    10.64714
        EUW19#21  |   10.78168   .4571239    23.59   0.000     9.883972    11.67939
        EUW19#22  |   10.43461   .7250103    14.39   0.000     9.010823     11.8584
        EUW19#23  |    9.66872   .6272905    15.41   0.000     8.436837     10.9006
        EUW19#24  |   8.237527   .7560901    10.89   0.000     6.752705    9.722349
        EUW19#25  |   8.494559   .7088161    11.98   0.000     7.102574    9.886543
        KOW20# 1  |  -8.442347   .8900595    -9.49   0.000    -10.19026   -6.694433
        KOW20# 2  |          0  (omitted)
        KOW20# 3  |          0  (omitted)
        KOW20# 4  |          0  (omitted)
        KOW20# 5  |          0  (omitted)
        KOW20# 6  |          0  (omitted)
        KOW20# 7  |          0  (omitted)
        KOW20# 8  |          0  (omitted)
        KOW20# 9  |          0  (omitted)
        KOW20#10  |          0  (omitted)
        KOW20#11  |          0  (omitted)
        KOW20#12  |          0  (omitted)
        KOW20#13  |          0  (omitted)
        KOW20#14  |          0  (omitted)
        KOW20#15  |          0  (omitted)
        KOW20#16  |          0  (omitted)
        KOW20#17  |          0  (omitted)
        KOW20#18  |          0  (omitted)
        KOW20#19  |          0  (omitted)
        KOW20#20  |          0  (omitted)
        KOW20#21  |          0  (omitted)
        KOW20#22  |          0  (omitted)
        KOW20#23  |          0  (omitted)
        KOW20#24  |          0  (omitted)
        KOW20#25  |          0  (omitted)
                  |
        ln_ew_ges |  -.7878053   1.025859    -0.77   0.443    -2.802403    1.226792
         ew_biodt |    .373603   .0301992    12.37   0.000     .3142973    .4329086
        ew_dtmihi |   .0630129   .0551351     1.14   0.254    -.0452624    .1712882
         ew_ledig |   .2127399   .0617424     3.45   0.001     .0914891    .3339906
       ew_married |   .4312672   .0634781     6.79   0.000     .3066079    .5559266
        wb_anteil |  -.2895078    .021973   -13.18   0.000    -.3326588   -.2463569
          wb_ausl |   .0153767   .0174803     0.88   0.379    -.0189514    .0497049
         wb_18t24 |  -.0164981   .0317896    -0.52   0.604     -.078927    .0459308
         wb_25t34 |  -.0724461    .020812    -3.48   0.001    -.1133171   -.0315752
         wb_35t44 |    .006101   .0247174     0.25   0.805    -.0424394    .0546413
         wb_45t59 |   .0140084   .0236709     0.59   0.554    -.0324769    .0604938
          avg_dur |  -.0288813   .0226638    -1.27   0.203    -.0733889    .0156263
          hh_kids |  -.0506236   .0439111    -1.15   0.249     -.136857    .0356098
mpreis_flats_rent |   .0303932   .0274072     1.11   0.268    -.0234294    .0842158
            _cons |   2.979287   9.750977     0.31   0.760    -16.16984    22.12841
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 229

           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.17)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.17)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.12)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.23)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.23)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.26)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                        X                 
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.19)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.19)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.14)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.26)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.26)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.27)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                    200+618             
            TW Cluster District-Election + Precinct                 X                 
            WRC Precinct                                                              
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  [0.946]             
            Reassignment (#t-3#)                                   -0.06              
                                                                  [0.749]             
            Reassignment (#t-2#)                                   0.01               
                                                                  [0.958]             
            Reassignment (#t+0#)                                   -1.00              
                                                                  [0.000]             
            Reassignment (#t+1#)                                   -0.89              
                                                                  [0.000]             
            Reassignment (#t+2#)                                   -0.75              
                                                                  [0.001]             
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                            X                 
           ----------------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)
note: 1b.wahl_id#3.stadtbez omitted because of collinearity
note: 1b.wahl_id#6.stadtbez omitted because of collinearity
note: 1b.wahl_id#14.stadtbez omitted because of collinearity
note: 1b.wahl_id#17.stadtbez omitted because of collinearity
note: 4.wahl_id#11.stadtbez omitted because of collinearity
note: 6.wahl_id#7.stadtbez omitted because of collinearity
note: 6.wahl_id#13.stadtbez omitted because of collinearity
note: 6.wahl_id#16.stadtbez omitted because of collinearity
note: 6.wahl_id#19.stadtbez omitted because of collinearity
note: 6.wahl_id#20.stadtbez omitted because of collinearity
note: 6.wahl_id#22.stadtbez omitted because of collinearity
note: 6.wahl_id#24.stadtbez omitted because of collinearity
note: 7.wahl_id#2.stadtbez omitted because of collinearity
note: 7.wahl_id#8.stadtbez omitted because of collinearity
note: 7.wahl_id#10.stadtbez omitted because of collinearity
note: 7.wahl_id#12.stadtbez omitted because of collinearity
note: 7.wahl_id#18.stadtbez omitted because of collinearity
note: 7.wahl_id#21.stadtbez omitted because of collinearity
note: 8.wahl_id#4.stadtbez omitted because of collinearity
note: 8.wahl_id#5.stadtbez omitted because of collinearity
note: 8.wahl_id#9.stadtbez omitted because of collinearity
note: 8.wahl_id#15.stadtbez omitted because of collinearity
note: 8.wahl_id#23.stadtbez omitted because of collinearity
note: 8.wahl_id#25.stadtbez omitted because of collinearity

Linear regression, absorbing indicators             Number of obs     =  4,666
Absorbed variable: sb_new                           No. of categories =    618
                                                    F(24, 24)         =      .
                                                    Prob > F          =      .
                                                    R-squared         = 0.9720
                                                    Adj R-squared     = 0.9660
                                                    Root MSE          = 1.6545

                                   (Std. err. adjusted for 25 clusters in stadtbez)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3115946    -0.53   0.599    -.8091463    .4770531
          F6event |   .0365542   .3205021     0.11   0.910    -.6249295    .6980379
          F5event |   .2427259   .3214015     0.76   0.457    -.4206143     .906066
          F4event |   .0132213   .2308041     0.06   0.955     -.463135    .4895776
          F3event |  -.0565081   .2047127    -0.28   0.785    -.4790143    .3659982
          F2event |    .006289    .127742     0.05   0.961    -.2573574    .2699355
          L0event |  -.9985596   .2873255    -3.48   0.002     -1.59157    -.405549
          L1event |   -.892731   .3019533    -2.96   0.007    -1.515932     -.26953
          L2event |  -.7515249   .3464222    -2.17   0.040    -1.466505   -.0365446
          L3event |  -.2931705   .3519135    -0.83   0.413    -1.019484    .4331432
          L4event |  -.8782313   .4572641    -1.92   0.067    -1.821978    .0655155
          L5event |  -.5487131   .4591155    -1.20   0.244    -1.496281    .3988548
          L6event |    1.07717   .8498761     1.27   0.217    -.6768878    2.831228
          L7event |   .9427971   1.059268     0.89   0.382    -1.243425    3.129019
                  |
 wahl_id#stadtbez |
        LTW13# 2  |   .8062484   .6168942     1.31   0.204    -.4669586    2.079455
        LTW13# 3  |          0  (omitted)
        LTW13# 4  |   8.627009   .5555927    15.53   0.000     7.480322    9.773696
        LTW13# 5  |   10.45435   .6031854    17.33   0.000      9.20944    11.69927
        LTW13# 6  |          0  (omitted)
        LTW13# 7  |  -5.447764   .3109282   -17.52   0.000    -6.089489    -4.80604
        LTW13# 8  |    .927322   .6239339     1.49   0.150    -.3604142    2.215058
        LTW13# 9  |   10.48862   .5561632    18.86   0.000      9.34076    11.63649
        LTW13#10  |   3.130943   .5506564     5.69   0.000     1.994444    4.267442
        LTW13#11  |   10.64304   .5722219    18.60   0.000     9.462028    11.82404
        LTW13#12  |   .5117469   .4476363     1.14   0.264    -.4121291    1.435623
        LTW13#13  |  -4.356591   .2986049   -14.59   0.000    -4.972881     -3.7403
        LTW13#14  |          0  (omitted)
        LTW13#15  |   12.46628   .4584096    27.19   0.000     11.52017    13.41239
        LTW13#16  |  -4.747632   .3001321   -15.82   0.000    -5.367075    -4.12819
        LTW13#17  |          0  (omitted)
        LTW13#18  |    .524777   .4462111     1.18   0.251    -.3961575    1.445711
        LTW13#19  |  -5.094838   .3041745   -16.75   0.000    -5.722623   -4.467053
        LTW13#20  |  -4.293382   .3438852   -12.48   0.000    -5.003126   -3.583638
        LTW13#21  |   1.156305   .4023743     2.87   0.008     .3258449    1.986764
        LTW13#22  |  -4.965527   .3704937   -13.40   0.000    -5.730188   -4.200866
        LTW13#23  |   13.56508   .4399713    30.83   0.000     12.65702    14.47314
        LTW13#24  |  -3.954945   .3487502   -11.34   0.000     -4.67473    -3.23516
        LTW13#25  |   10.69362   .4652734    22.98   0.000     9.733347     11.6539
        BTW13# 1  |   5.179102   .0736876    70.28   0.000     5.027019    5.331186
        BTW13# 2  |   5.373972   .6200422     8.67   0.000     4.094268    6.653676
        BTW13# 3  |    5.28951   .0787048    67.21   0.000     5.127072    5.451949
        BTW13# 4  |   13.95461   .5568792    25.06   0.000     12.80527    15.10395
        BTW13# 5  |   14.92427   .6067069    24.60   0.000     13.67208    16.17645
        BTW13# 6  |   4.530733   .0594692    76.19   0.000     4.407995    4.653472
        BTW13# 7  |   -.785203   .3041666    -2.58   0.016    -1.412972   -.1574339
        BTW13# 8  |   5.765989   .6163654     9.35   0.000     4.493873    7.038104
        BTW13# 9  |   15.47349   .5544257    27.91   0.000     14.32921    16.61777
        BTW13#10  |   8.568143   .5432014    15.77   0.000     7.447031    9.689256
        BTW13#11  |   16.60336   .5639446    29.44   0.000     15.43943    17.76728
        BTW13#12  |   5.695418   .4434571    12.84   0.000     4.780167    6.610668
        BTW13#13  |   .7531959   .2952735     2.55   0.018     .1437813     1.36261
        BTW13#14  |   5.432726   .0301918   179.94   0.000     5.370413    5.495039
        BTW13#15  |   17.47129   .4549609    38.40   0.000     16.53229    18.41028
        BTW13#16  |   .6253258   .2967526     2.11   0.046     .0128585    1.237793
        BTW13#17  |   5.491525   .0382444   143.59   0.000     5.412592    5.570457
        BTW13#18  |   5.081985   .4488948    11.32   0.000     4.155511    6.008458
        BTW13#19  |  -.0141957   .2982799    -0.05   0.962    -.6298152    .6014239
        BTW13#20  |   .3522016   .3377884     1.04   0.307    -.3449595    1.049363
        BTW13#21  |   5.400168   .3957066    13.65   0.000      4.58347    6.216867
        BTW13#22  |  -.0069411   .3646244    -0.02   0.985     -.759489    .7456067
        BTW13#23  |   17.75538   .4213028    42.14   0.000     16.88585    18.62491
        BTW13#24  |   1.073383   .3459046     3.10   0.005     .3594712    1.787295
        BTW13#25  |    15.5023   .4647128    33.36   0.000     14.54318    16.46142
        KOW14# 1  |  -6.461544   .4242713   -15.23   0.000    -7.337197   -5.585891
        KOW14# 2  |  -7.374477   .4919094   -14.99   0.000    -8.389728   -6.359226
        KOW14# 3  |   -7.84998   .4022795   -19.51   0.000    -8.680244   -7.019716
        KOW14# 4  |   1.543643   .4490767     3.44   0.002     .6167942    2.470492
        KOW14# 5  |   1.856712   .4951839     3.75   0.001     .8347029    2.878721
        KOW14# 6  |   -9.27393   .4168818   -22.25   0.000    -10.13433   -8.413528
        KOW14# 7  |  -14.60143   .3456018   -42.25   0.000    -15.31472   -13.88815
        KOW14# 8  |  -8.066593    .448191   -18.00   0.000    -8.991614   -7.141572
        KOW14# 9  |   2.560967   .4471606     5.73   0.000     1.638073    3.483861
        KOW14#10  |  -6.709959   .3580979   -18.74   0.000    -7.449037   -5.970882
        KOW14#11  |   2.091786   .3746023     5.58   0.000     1.318645    2.864927
        KOW14#12  |  -8.109408   .3182872   -25.48   0.000    -8.766321   -7.452496
        KOW14#13  |  -13.80926   .2822278   -48.93   0.000    -14.39175   -13.22677
        KOW14#14  |    -10.053   .3940539   -25.51   0.000    -10.86629   -9.239715
        KOW14#15  |   .4702327    .327837     1.43   0.164    -.2063897    1.146855
        KOW14#16  |  -14.87525   .3217419   -46.23   0.000    -15.53929   -14.21121
        KOW14#17  |  -9.372424   .4340924   -21.59   0.000    -10.26835   -8.476502
        KOW14#18  |  -7.809946    .354473   -22.03   0.000    -8.541542   -7.078349
        KOW14#19  |  -14.39733   .3023748   -47.61   0.000     -15.0214   -13.77326
        KOW14#20  |  -14.97745   .3055112   -49.02   0.000    -15.60799    -14.3469
        KOW14#21  |  -8.237277   .2669652   -30.86   0.000    -8.788266   -7.686288
        KOW14#22  |  -16.52786   .2931396   -56.38   0.000    -17.13287   -15.92285
        KOW14#23  |   3.058166   .2540665    12.04   0.000     2.533798    3.582533
        KOW14#24  |  -15.69557   .3317621   -47.31   0.000     -16.3803   -15.01085
        KOW14#25  |   1.100464   .2987853     3.68   0.001     .4838015    1.717127
        EUW14# 1  |  -8.304891   .4141401   -20.05   0.000    -9.159634   -7.450148
        EUW14# 2  |  -8.377857   .4171273   -20.08   0.000    -9.238765   -7.516948
        EUW14# 3  |    -8.4436   .4025245   -20.98   0.000     -9.27437   -7.612831
        EUW14# 4  |   -.086198   .5112781    -0.17   0.868    -1.141424    .9690281
        EUW14# 5  |  -.1612736   .5722927    -0.28   0.781    -1.342428     1.01988
        EUW14# 6  |  -11.26777   .4034341   -27.93   0.000    -12.10042   -10.43512
        EUW14# 7  |   -17.2315   .3833936   -44.94   0.000    -18.02278   -16.44021
        EUW14# 8  |  -10.52744   .2786101   -37.79   0.000    -11.10246   -9.952414
        EUW14# 9  |    .113158   .4960954     0.23   0.822    -.9107325    1.137049
        EUW14#10  |  -10.11896   .2716929   -37.24   0.000    -10.67971   -9.558213
        EUW14#11  |          0  (omitted)
        EUW14#12  |  -9.597914   .2371599   -40.47   0.000    -10.08739    -9.10844
        EUW14#13  |  -16.44884   .3148144   -52.25   0.000    -17.09858   -15.79909
        EUW14#14  |  -11.89884   .4023324   -29.57   0.000    -12.72921   -11.06846
        EUW14#15  |  -2.504358   .4005132    -6.25   0.000    -3.330976   -1.677739
        EUW14#16  |  -17.71723   .3660153   -48.41   0.000    -18.47265   -16.96181
        EUW14#17  |  -11.82529   .4361107   -27.12   0.000    -12.72538    -10.9252
        EUW14#18  |  -9.485629   .2593259   -36.58   0.000    -10.02085   -8.950407
        EUW14#19  |  -16.95363   .3165355   -53.56   0.000    -17.60692   -16.30033
        EUW14#20  |   -17.0182   .3053392   -55.74   0.000    -17.64839   -16.38801
        EUW14#21  |  -10.88291   .2179376   -49.94   0.000    -11.33272   -10.43311
        EUW14#22  |   -20.0588   .3241139   -61.89   0.000    -20.72773   -19.38986
        EUW14#23  |  -1.869141   .3434585    -5.44   0.000    -2.578004   -1.160277
        EUW14#24  |  -19.07036   .3665341   -52.03   0.000    -19.82685   -18.31387
        EUW14#25  |  -1.174533   .4049843    -2.90   0.008    -2.010379   -.3386861
        BTW17# 1  |   6.623327   .3186908    20.78   0.000     5.965581    7.281072
        BTW17# 2  |   7.246935   .4983857    14.54   0.000     6.218318    8.275553
        BTW17# 3  |   7.963282   .3414953    23.32   0.000     7.258471    8.668094
        BTW17# 4  |   16.91978   .4229521    40.00   0.000     16.04685    17.79271
        BTW17# 5  |     16.596   .4663176    35.59   0.000     15.63357    17.55844
        BTW17# 6  |    7.45176   .2386519    31.22   0.000     6.959207    7.944314
        BTW17# 7  |    3.31932   .1921752    17.27   0.000      2.92269     3.71595
        BTW17# 8  |   8.764557   .5729464    15.30   0.000     7.582053     9.94706
        BTW17# 9  |    18.3968   .4091359    44.97   0.000     17.55239    19.24122
        BTW17#10  |   11.88411   .4961438    23.95   0.000     10.86012     12.9081
        BTW17#11  |      20.78   .6210734    33.46   0.000     19.49816    22.06183
        BTW17#12  |   8.739127   .4370932    19.99   0.000     7.837011    9.641243
        BTW17#13  |   2.180705    .257108     8.48   0.000      1.65006     2.71135
        BTW17#14  |   7.631436   .2054294    37.15   0.000     7.207451    8.055422
        BTW17#15  |   21.29595   .3570877    59.64   0.000     20.55896    22.03294
        BTW17#16  |   2.985234   .2455466    12.16   0.000     2.478451    3.492017
        BTW17#17  |   7.438641   .1769464    42.04   0.000     7.073442    7.803841
        BTW17#18  |   7.831034   .4343303    18.03   0.000      6.93462    8.727447
        BTW17#19  |   2.347621   .2609437     9.00   0.000      1.80906    2.886183
        BTW17#20  |   3.807503   .3071226    12.40   0.000     3.173633    4.441373
        BTW17#21  |   9.007838   .3667324    24.56   0.000      8.25094    9.764737
        BTW17#22  |   3.606831   .3100166    11.63   0.000     2.966988    4.246674
        BTW17#23  |   20.17909   .3851247    52.40   0.000     19.38423    20.97395
        BTW17#24  |   4.529306   .2894882    15.65   0.000     3.931832    5.126781
        BTW17#25  |   18.61648   .4211296    44.21   0.000     17.74731    19.48565
        LTW18# 1  |   4.247703   .3937128    10.79   0.000      3.43512    5.060287
        LTW18# 2  |   4.781085   .4451341    10.74   0.000     3.862373    5.699797
        LTW18# 3  |   6.016288   .4019474    14.97   0.000      5.18671    6.845867
        LTW18# 4  |   14.80755   .3125133    47.38   0.000     14.16256    15.45255
        LTW18# 5  |   13.22889   .4095241    32.30   0.000     12.38367     14.0741
        LTW18# 6  |   4.571058   .2812223    16.25   0.000     3.990643    5.151472
        LTW18# 7  |          0  (omitted)
        LTW18# 8  |   5.838108   .6118633     9.54   0.000     4.575284    7.100932
        LTW18# 9  |   15.14662   .3210505    47.18   0.000       14.484    15.80923
        LTW18#10  |   7.589411    .401327    18.91   0.000     6.761112    8.417709
        LTW18#11  |   16.40694   .5372117    30.54   0.000     15.29819    17.51569
        LTW18#12  |    5.94279   .3331007    17.84   0.000     5.255304    6.630276
        LTW18#13  |          0  (omitted)
        LTW18#14  |   3.567666   .2936294    12.15   0.000     2.961645    4.173687
        LTW18#15  |   18.17208   .3290901    55.22   0.000     17.49288    18.85129
        LTW18#16  |          0  (omitted)
        LTW18#17  |    4.67752   .2447677    19.11   0.000     4.172344    5.182696
        LTW18#18  |   7.031867   .3447589    20.40   0.000      6.32032    7.743414
        LTW18#19  |          0  (omitted)
        LTW18#20  |          0  (omitted)
        LTW18#21  |    6.50063   .2952374    22.02   0.000      5.89129     7.10997
        LTW18#22  |          0  (omitted)
        LTW18#23  |   17.69707   .3489929    50.71   0.000     16.97679    18.41736
        LTW18#24  |          0  (omitted)
        LTW18#25  |   14.83052   .3399047    43.63   0.000     14.12899    15.53205
        EUW19# 1  |  -.5317836   .7060469    -0.75   0.459    -1.988993    .9254257
        EUW19# 2  |          0  (omitted)
        EUW19# 3  |   1.558738   .6018913     2.59   0.016     .3164957    2.800981
        EUW19# 4  |   9.794111   .2441316    40.12   0.000     9.290249    10.29797
        EUW19# 5  |   8.607378   .3582118    24.03   0.000     7.868065    9.346691
        EUW19# 6  |  -.2792804   .5402506    -0.52   0.610    -1.394303    .8357421
        EUW19# 7  |  -6.200777   .4019689   -15.43   0.000      -7.0304   -5.371154
        EUW19# 8  |          0  (omitted)
        EUW19# 9  |   8.236168   .2917254    28.23   0.000     7.634076    8.838259
        EUW19#10  |          0  (omitted)
        EUW19#11  |   9.836932   .3170222    31.03   0.000      9.18263    10.49123
        EUW19#12  |          0  (omitted)
        EUW19#13  |  -5.780082    .309404   -18.68   0.000    -6.418661   -5.141504
        EUW19#14  |  -1.715355   .5596261    -3.07   0.005    -2.870367   -.5603436
        EUW19#15  |   10.76622   .2337184    46.06   0.000     10.28385    11.24859
        EUW19#16  |   -6.85083   .3962397   -17.29   0.000    -7.668628   -6.033031
        EUW19#17  |  -1.867844   .5462442    -3.42   0.002    -2.995237   -.7404519
        EUW19#18  |          0  (omitted)
        EUW19#19  |  -6.960975   .3052386   -22.81   0.000    -7.590957   -6.330994
        EUW19#20  |  -6.322863   .3127231   -20.22   0.000    -6.968292   -5.677435
        EUW19#21  |          0  (omitted)
        EUW19#22  |  -8.999949   .3033755   -29.67   0.000    -9.626085   -8.373812
        EUW19#23  |    9.66872     .26189    36.92   0.000     9.128205    10.20923
        EUW19#24  |  -7.849234   .3914088   -20.05   0.000    -8.657062   -7.041406
        EUW19#25  |   8.494559   .2760033    30.78   0.000     7.924916    9.064201
        KOW20# 1  |  -8.442347   .7026452   -12.02   0.000    -9.892535   -6.992158
        KOW20# 2  |  -8.878702   .2975704   -29.84   0.000    -9.492857   -8.264547
        KOW20# 3  |  -7.399244   .6056372   -12.22   0.000    -8.649218    -6.14927
        KOW20# 4  |          0  (omitted)
        KOW20# 5  |          0  (omitted)
        KOW20# 6  |  -9.432198   .5760269   -16.37   0.000    -10.62106   -8.243337
        KOW20# 7  |  -15.07874   .3706312   -40.68   0.000    -15.84369    -14.3138
        KOW20# 8  |  -8.449083   .3776253   -22.37   0.000    -9.228463   -7.669703
        KOW20# 9  |          0  (omitted)
        KOW20#10  |  -9.155411   .3444193   -26.58   0.000    -9.866257   -8.444564
        KOW20#11  |   1.141868   .5072605     2.25   0.034     .0949336    2.188802
        KOW20#12  |  -8.757942   .2500143   -35.03   0.000    -9.273947   -8.241938
        KOW20#13  |  -16.85409   .2991729   -56.34   0.000    -17.47155   -16.23663
        KOW20#14  |  -9.835615   .5539273   -17.76   0.000    -10.97887   -8.692366
        KOW20#15  |          0  (omitted)
        KOW20#16  |     -15.54   .3663054   -42.42   0.000    -16.29602   -14.78398
        KOW20#17  |  -10.09305   .5685834   -17.75   0.000    -11.26655    -8.91955
        KOW20#18  |  -8.457362   .2787095   -30.34   0.000     -9.03259   -7.882134
        KOW20#19  |  -16.45795   .3313061   -49.68   0.000    -17.14173   -15.77417
        KOW20#20  |  -15.57061   .3257297   -47.80   0.000    -16.24288   -14.89834
        KOW20#21  |  -10.78168   .2303035   -46.82   0.000      -11.257   -10.30636
        KOW20#22  |  -19.43456   .3246034   -59.87   0.000    -20.10451   -18.76461
        KOW20#23  |          0  (omitted)
        KOW20#24  |  -16.08676   .3717917   -43.27   0.000     -16.8541   -15.31942
        KOW20#25  |          0  (omitted)
                  |
        ln_ew_ges |  -.7878053   .8997319    -0.88   0.390    -2.644761     1.06915
         ew_biodt |    .373603   .0314507    11.88   0.000      .308692    .4385139
        ew_dtmihi |   .0630129   .0645672     0.98   0.339    -.0702473    .1962732
         ew_ledig |   .2127399   .0673508     3.16   0.004     .0737346    .3517452
       ew_married |   .4312672   .0766019     5.63   0.000     .2731687    .5893658
        wb_anteil |  -.2895078   .0240849   -12.02   0.000    -.3392166   -.2397991
          wb_ausl |   .0153767   .0227867     0.67   0.506    -.0316527    .0624062
         wb_18t24 |  -.0164981   .0340966    -0.48   0.633      -.08687    .0538737
         wb_25t34 |  -.0724461   .0248693    -2.91   0.008    -.1237739   -.0211184
         wb_35t44 |    .006101   .0185977     0.33   0.746    -.0322828    .0444848
         wb_45t59 |   .0140084   .0165405     0.85   0.405    -.0201296    .0481464
          avg_dur |  -.0288813   .0259611    -1.11   0.277    -.0824623    .0246997
          hh_kids |  -.0506236   .0446868    -1.13   0.268    -.1428526    .0416053
mpreis_flats_rent |   .0303932   .0272808     1.11   0.276    -.0259115    .0866979
            _cons |   11.68799   8.647453     1.35   0.189    -6.159477    29.53546
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 229

           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.17)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.17)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.12)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.23)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.23)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.26)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                        X                 
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
            WRC District                                                              
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.19)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.19)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.14)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.26)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.26)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.27)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                    200+618             
            TW Cluster District-Election + Precinct                 X                 
            WRC Precinct                                                              
            WRC District                                                              
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  [0.946]             
            Reassignment (#t-3#)                                   -0.06              
                                                                  [0.749]             
            Reassignment (#t-2#)                                   0.01               
                                                                  [0.958]             
            Reassignment (#t+0#)                                   -1.00              
                                                                  [0.000]             
            Reassignment (#t+1#)                                   -0.89              
                                                                  [0.000]             
            Reassignment (#t+2#)                                   -0.75              
                                                                  [0.001]             
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                            X                 
            WRC District                                                              
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  [0.946]             
            Reassignment (#t-3#)                                   -0.06              
                                                                  [0.769]             
            Reassignment (#t-2#)                                   0.01               
                                                                  [0.961]             
            Reassignment (#t+0#)                                   -1.00              
                                                                  [0.000]             
            Reassignment (#t+1#)                                   -0.89              
                                                                  [0.002]             
            Reassignment (#t+2#)                                   -0.75              
                                                                  [0.031]             
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      25                
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
            WRC District                                            X                 
           ----------------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)

Linear regression, absorbing indicators             Number of obs     =  4,666
Absorbed variable: sb_new                           No. of categories =    618
                                                    F(24, 24)         =      .
                                                    Prob > F          =      .
                                                    R-squared         = 0.9633
                                                    Adj R-squared     = 0.9573
                                                    Root MSE          = 1.8937

                                   (Std. err. adjusted for 25 clusters in stadtbez)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.2719899   .3804467    -0.71   0.482    -1.057193    .5132134
          F6event |  -.1600481   .3385224    -0.47   0.641    -.8587238    .5386277
          F5event |    .106833   .3259324     0.33   0.746    -.5658585    .7795244
          F4event |  -.1151478   .1928458    -0.60   0.556    -.5131619    .2828664
          F3event |  -.0375634   .2160543    -0.17   0.863    -.4834775    .4083508
          F2event |   .1549506   .1641357     0.94   0.355    -.1838089    .4937101
          L0event |  -1.068154   .2603035    -4.10   0.000    -1.605394   -.5309143
          L1event |  -.8724909   .3533453    -2.47   0.021     -1.60176    -.143222
          L2event |  -.7015394   .3480054    -2.02   0.055    -1.419787    .0167083
          L3event |  -.0880174   .3519991    -0.25   0.805    -.8145079    .6384731
          L4event |  -.1994726   .6539257    -0.31   0.763    -1.549109    1.150164
          L5event |   .4861285   .9954096     0.49   0.630    -1.568296    2.540553
          L6event |   .4275625   1.061003     0.40   0.691    -1.762241    2.617366
          L7event |   .9091168   .9393155     0.97   0.343    -1.029535    2.847769
                  |
          wahl_id |
           BTW13  |   4.936167   .1165554    42.35   0.000     4.695609    5.176726
           KOW14  |  -10.05969   .6540789   -15.38   0.000    -11.40965    -8.70974
           EUW14  |  -11.88159   .6404142   -18.55   0.000    -13.20334   -10.55984
           BTW17  |   7.695129   .2653249    29.00   0.000     7.147526    8.242733
           LTW18  |   4.630091   .2957147    15.66   0.000     4.019766    5.240416
           EUW19  |  -1.817324   .5827098    -3.12   0.005    -3.019978   -.6146704
           KOW20  |  -11.58195   .7537854   -15.37   0.000    -13.13768   -10.02621
                  |
        ln_ew_ges |  -.7462869   1.171516    -0.64   0.530    -3.164177    1.671604
         ew_biodt |   .3520456   .0321239    10.96   0.000      .285745    .4183462
        ew_dtmihi |   .0613483   .0629803     0.97   0.340    -.0686367    .1913334
         ew_ledig |    .222933   .0638682     3.49   0.002     .0911155    .3547505
       ew_married |   .4248988   .0697115     6.10   0.000     .2810213    .5687763
        wb_anteil |  -.2432859   .0290953    -8.36   0.000    -.3033357   -.1832361
          wb_ausl |   .0212218   .0207889     1.02   0.318    -.0216844     .064128
         wb_18t24 |  -.0462596   .0377185    -1.23   0.232    -.1241069    .0315876
         wb_25t34 |  -.0432859   .0223847    -1.93   0.065    -.0894857    .0029138
         wb_35t44 |    .002344   .0229767     0.10   0.920    -.0450775    .0497655
         wb_45t59 |    .015456   .0187084     0.83   0.417    -.0231563    .0540682
          avg_dur |  -.0193425   .0261343    -0.74   0.466    -.0732809     .034596
          hh_kids |  -.0496397   .0467751    -1.06   0.299    -.1461788    .0468994
mpreis_flats_rent |   .0512944   .0232362     2.21   0.037     .0033372    .0992516
            _cons |   10.57306   9.895853     1.07   0.296    -9.850978     30.9971
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 37

           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.17)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.17)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.12)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.23)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.23)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.26)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                        X                 
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
            WRC District                                                              
            Election FE                                                               
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.19)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.19)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.14)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.26)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.26)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.27)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                    200+618             
            TW Cluster District-Election + Precinct                 X                 
            WRC Precinct                                                              
            WRC District                                                              
            Election FE                                                               
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  [0.946]             
            Reassignment (#t-3#)                                   -0.06              
                                                                  [0.749]             
            Reassignment (#t-2#)                                   0.01               
                                                                  [0.958]             
            Reassignment (#t+0#)                                   -1.00              
                                                                  [0.000]             
            Reassignment (#t+1#)                                   -0.89              
                                                                  [0.000]             
            Reassignment (#t+2#)                                   -0.75              
                                                                  [0.001]             
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                            X                 
            WRC District                                                              
            Election FE                                                               
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  [0.946]             
            Reassignment (#t-3#)                                   -0.06              
                                                                  [0.769]             
            Reassignment (#t-2#)                                   0.01               
                                                                  [0.961]             
            Reassignment (#t+0#)                                   -1.00              
                                                                  [0.000]             
            Reassignment (#t+1#)                                   -0.89              
                                                                  [0.002]             
            Reassignment (#t+2#)                                   -0.75              
                                                                  [0.031]             
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      25                
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
            WRC District                                            X                 
            Election FE                                                               
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   -0.12              
                                                                  [0.530]             
            Reassignment (#t-3#)                                   -0.04              
                                                                  [0.850]             
            Reassignment (#t-2#)                                   0.15               
                                                                  [0.348]             
            Reassignment (#t+0#)                                   -1.07              
                                                                  [0.001]             
            Reassignment (#t+1#)                                   -0.87              
                                                                  [0.029]             
            Reassignment (#t+2#)                                   -0.70              
                                                                  [0.052]             
            R2                                                     0.96               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                                      
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      25                
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
            WRC District                                            X                 
            Election FE                                             X                 
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.17)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.17)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.12)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.23)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.23)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.26)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                        X                 
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
            WRC District                                                              
            Election FE                                                               
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  (0.19)              
            Reassignment (#t-3#)                                   -0.06              
                                                                  (0.19)              
            Reassignment (#t-2#)                                   0.01               
                                                                  (0.14)              
            Reassignment (#t+0#)                                 -1.00***             
                                                                  (0.26)              
            Reassignment (#t+1#)                                 -0.89***             
                                                                  (0.26)              
            Reassignment (#t+2#)                                  -0.75**             
                                                                  (0.27)              
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                    200+618             
            TW Cluster District-Election + Precinct                 X                 
            WRC Precinct                                                              
            WRC District                                                              
            Election FE                                                               
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  [0.946]             
            Reassignment (#t-3#)                                   -0.06              
                                                                  [0.749]             
            Reassignment (#t-2#)                                   0.01               
                                                                  [0.958]             
            Reassignment (#t+0#)                                   -1.00              
                                                                  [0.000]             
            Reassignment (#t+1#)                                   -0.89              
                                                                  [0.000]             
            Reassignment (#t+2#)                                   -0.75              
                                                                  [0.001]             
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      618               
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                            X                 
            WRC District                                                              
            Election FE                                                               
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   0.01               
                                                                  [0.946]             
            Reassignment (#t-3#)                                   -0.06              
                                                                  [0.769]             
            Reassignment (#t-2#)                                   0.01               
                                                                  [0.961]             
            Reassignment (#t+0#)                                   -1.00              
                                                                  [0.000]             
            Reassignment (#t+1#)                                   -0.89              
                                                                  [0.002]             
            Reassignment (#t+2#)                                   -0.75              
                                                                  [0.031]             
            R2                                                     0.97               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                    X                 
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      25                
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
            WRC District                                            X                 
            Election FE                                                               
           ----------------------------------------------------------------------------


           ----------------------------------------------------------------------------
                                                      Effect on polling place turnout 
           ----------------------------------------------------------------------------
            Reassignment (#t-4#)                                   -0.12              
                                                                  [0.530]             
            Reassignment (#t-3#)                                   -0.04              
                                                                  [0.850]             
            Reassignment (#t-2#)                                   0.15               
                                                                  [0.348]             
            Reassignment (#t+0#)                                   -1.07              
                                                                  [0.001]             
            Reassignment (#t+1#)                                   -0.87              
                                                                  [0.029]             
            Reassignment (#t+2#)                                   -0.70              
                                                                  [0.052]             
            R2                                                     0.96               
            N                                                      4,666              
            Precinct FE                                             X                 
            Election-District FE                                                      
            Election-District FE                                                      
            Cluster Precinct                                                          
            Number of Clusters                                      25                
            TW Cluster District-Election + Precinct                                   
            WRC Precinct                                                              
            WRC District                                            X                 
            Election FE                                             X                 
           ----------------------------------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9616
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2040
Number of clusters (sb_new)  =        618         Root MSE        =     1.6847

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3314703     0.25   0.801    -.5673098    .7345839
          F6event |   .2184141   .2600152     0.84   0.401    -.2922081    .7290362
          F5event |  -.4796951   .2637694    -1.82   0.069    -.9976898    .0382995
          F4event |  -.2325114   .1614186    -1.44   0.150    -.5495078     .084485
          F3event |   .0074853   .1524588     0.05   0.961    -.2919157    .3068862
          F2event |   -.059996   .1248273    -0.48   0.631    -.3051339    .1851419
          L0event |   .6092887   .2189284     2.78   0.006     .1793535    1.039224
          L1event |   .9038027   .2273231     3.98   0.000      .457382    1.350223
          L2event |   1.047491   .2631015     3.98   0.000     .5308081    1.564174
          L3event |   .4175165   .2577372     1.62   0.106    -.0886321    .9236651
          L4event |   1.531036   .6502778     2.35   0.019     .2540104    2.808063
          L5event |   2.356691   .5459981     4.32   0.000     1.284451    3.428931
          L6event |  -.3848372    .877942    -0.44   0.661    -2.108954     1.33928
          L7event |   -.503072    .765562    -0.66   0.511    -2.006495    1.000351
        ln_ew_ges |   2.465914   1.349016     1.83   0.068    -.1833058    5.115133
         ew_biodt |    .388145   .0296323    13.10   0.000     .3299526    .4463375
        ew_dtmihi |  -.2303872   .0595829    -3.87   0.000     -.347397   -.1133774
         ew_ledig |   .2111155   .0802108     2.63   0.009     .0535962    .3686348
       ew_married |   .2074512   .0799261     2.60   0.010     .0504911    .3644114
        wb_anteil |  -.2425344   .0223631   -10.85   0.000    -.2864513   -.1986174
          wb_ausl |   -.068913   .0145836    -4.73   0.000    -.0975526   -.0402734
         wb_18t24 |  -.0273058   .0275422    -0.99   0.322    -.0813937    .0267821
         wb_25t34 |   .0526598   .0194716     2.70   0.007     .0144212    .0908985
         wb_35t44 |  -.0089296   .0249211    -0.36   0.720    -.0578702    .0400109
         wb_45t59 |   -.038132   .0205362    -1.86   0.064    -.0784614    .0021973
          avg_dur |   .0459808   .0233899     1.97   0.050     .0000472    .0919144
          hh_kids |  -.0642629   .0417916    -1.54   0.125    -.1463339    .0178081
mpreis_flats_rent |  -.0155286   .0234069    -0.66   0.507    -.0614954    .0304383
            _cons |  -11.72708   11.35784    -1.03   0.302     -34.0318    10.57764
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: matrix in addrows option has varying size rows:
   `"R2 "',`"0.96"' \ `"N"',`"4,666"'\Precinct FE, X \ Election-District FE, X \ Election-District
>  FE, \ Cluster Precinct, X \ Number of Clusters, 618
warning: no existing table found for merge or append

                       ---------------------------------------------------
                                               Effect on mail-in turnout 
                       ---------------------------------------------------
                        Reassignment (#t-4#)             -0.23           
                                                        (0.16)           
                        Reassignment (#t-3#)             0.01            
                                                        (0.15)           
                        Reassignment (#t-2#)             -0.06           
                                                        (0.12)           
                        Reassignment (#t+0#)            0.61**           
                                                        (0.22)           
                        Reassignment (#t+1#)            0.90***          
                                                        (0.23)           
                        Reassignment (#t+2#)            1.05***          
                                                        (0.26)           
                        R2                               0.96            
                        N                                4,666           
                        Precinct FE                       X              
                        Election-District FE              X              
                        Election-District FE                             
                        Cluster Precinct                  X              
                        Number of Clusters                618            
                       ---------------------------------------------------

(MWFE estimator converged in 8 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller appl
> ied.

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    199) =      14.59
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9616
                                                  Adj R-squared   =     0.9531
Number of clusters (sb_new)  =        618         Within R-sq.    =     0.2040
Number of clusters (stadtbez#wahl_id) =        200Root MSE        =     1.6850

                   (Std. err. adjusted for 200 clusters in sb_new stadtbez#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3277361     0.26   0.799    -.5626443    .7299183
          F6event |   .2184141   .2724769     0.80   0.424    -.3188985    .7557266
          F5event |  -.4796951   .2641024    -1.82   0.071    -1.000494    .0411034
          F4event |  -.2325114   .1624971    -1.43   0.154    -.5529486    .0879257
          F3event |   .0074853   .1585492     0.05   0.962    -.3051668    .3201373
          F2event |   -.059996   .1420153    -0.42   0.673     -.340044    .2200521
          L0event |   .6092887   .2329401     2.62   0.010      .149941    1.068636
          L1event |   .9038027   .2457519     3.68   0.000     .4191906    1.388415
          L2event |   1.047491   .2748067     3.81   0.000     .5055843    1.589398
          L3event |   .4175165   .2966171     1.41   0.161    -.1673996    1.002433
          L4event |   1.531036   .7480206     2.05   0.042     .0559723    3.006101
          L5event |   2.356691   .4576841     5.15   0.000     1.454158    3.259224
          L6event |  -.3848372    .792447    -0.49   0.628    -1.947508    1.177834
          L7event |   -.503072   .6917466    -0.73   0.468    -1.867166    .8610224
        ln_ew_ges |   2.465914   1.324902     1.86   0.064    -.1467354    5.078563
         ew_biodt |    .388145   .0316082    12.28   0.000      .325815    .4504751
        ew_dtmihi |  -.2303872   .0654951    -3.52   0.001    -.3595407   -.1012337
         ew_ledig |   .2111155   .0738308     2.86   0.005     .0655243    .3567067
       ew_married |   .2074512   .0740582     2.80   0.006     .0614117    .3534908
        wb_anteil |  -.2425344   .0277677    -8.73   0.000    -.2972911   -.1877776
          wb_ausl |   -.068913   .0158167    -4.36   0.000    -.1001029   -.0377231
         wb_18t24 |  -.0273058   .0265449    -1.03   0.305    -.0796512    .0250396
         wb_25t34 |   .0526598   .0182423     2.89   0.004     .0166869    .0886327
         wb_35t44 |  -.0089296   .0283082    -0.32   0.753    -.0647521    .0468929
         wb_45t59 |   -.038132   .0210778    -1.81   0.072    -.0796965    .0034325
          avg_dur |   .0459808   .0232862     1.97   0.050     .0000614    .0919002
          hh_kids |  -.0642629   .0430279    -1.49   0.137     -.149112    .0205862
mpreis_flats_rent |  -.0155286   .0224946    -0.69   0.491    -.0598869    .0288297
            _cons |  -11.72708   11.25921    -1.04   0.299    -33.92975    10.47558
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200         200           0    *|
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

-------------------------------------------------------------------------------------------------
                                           Effect on mail-in turnout  Effect on mail-in turnout 
-------------------------------------------------------------------------------------------------
 Reassignment (#t-4#)                                -0.23                      -0.23           
                                                    (0.16)                     (0.16)           
 Reassignment (#t-3#)                                0.01                       0.01            
                                                    (0.15)                     (0.16)           
 Reassignment (#t-2#)                                -0.06                      -0.06           
                                                    (0.12)                     (0.14)           
 Reassignment (#t+0#)                               0.61**                     0.61**           
                                                    (0.22)                     (0.23)           
 Reassignment (#t+1#)                               0.90***                    0.90***          
                                                    (0.23)                     (0.25)           
 Reassignment (#t+2#)                               1.05***                    1.05***          
                                                    (0.26)                     (0.27)           
 R2                                                  0.96                       0.96            
 N                                                   4,666                      4,666           
 Precinct FE                                          X                          X              
 Election-District FE                                 X                          X              
 Election-District FE                                                                           
 Cluster Precinct                                     X                                         
 Number of Clusters                                   618                      200+618          
 TW Cluster District-Election + Precinct                                         X              
-------------------------------------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)
note: 8.wahl_id#2.stadtbez omitted because of collinearity.
note: 8.wahl_id#3.stadtbez omitted because of collinearity.
note: 8.wahl_id#4.stadtbez omitted because of collinearity.
note: 8.wahl_id#5.stadtbez omitted because of collinearity.
note: 8.wahl_id#6.stadtbez omitted because of collinearity.
note: 8.wahl_id#7.stadtbez omitted because of collinearity.
note: 8.wahl_id#8.stadtbez omitted because of collinearity.
note: 8.wahl_id#9.stadtbez omitted because of collinearity.
note: 8.wahl_id#10.stadtbez omitted because of collinearity.
note: 8.wahl_id#11.stadtbez omitted because of collinearity.
note: 8.wahl_id#12.stadtbez omitted because of collinearity.
note: 8.wahl_id#13.stadtbez omitted because of collinearity.
note: 8.wahl_id#14.stadtbez omitted because of collinearity.
note: 8.wahl_id#15.stadtbez omitted because of collinearity.
note: 8.wahl_id#16.stadtbez omitted because of collinearity.
note: 8.wahl_id#17.stadtbez omitted because of collinearity.
note: 8.wahl_id#18.stadtbez omitted because of collinearity.
note: 8.wahl_id#19.stadtbez omitted because of collinearity.
note: 8.wahl_id#20.stadtbez omitted because of collinearity.
note: 8.wahl_id#21.stadtbez omitted because of collinearity.
note: 8.wahl_id#22.stadtbez omitted because of collinearity.
note: 8.wahl_id#23.stadtbez omitted because of collinearity.
note: 8.wahl_id#24.stadtbez omitted because of collinearity.
note: 8.wahl_id#25.stadtbez omitted because of collinearity.

Linear regression, absorbing indicators             Number of obs     =  4,666
Absorbed variable: sb_new                           No. of categories =    618
                                                    F(203, 617)       = 459.73
                                                    Prob > F          = 0.0000
                                                    R-squared         = 0.9616
                                                    Adj R-squared     = 0.9534
                                                    Root MSE          = 1.6795

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3561149     0.23   0.814    -.6157072    .7829813
          F6event |   .2184141   .2793472     0.78   0.435    -.3301725    .7670006
          F5event |  -.4796951   .2833805    -1.69   0.091    -1.036202     .076812
          F4event |  -.2325114   .1734199    -1.34   0.180    -.5730763    .1080534
          F3event |   .0074853   .1637939     0.05   0.964     -.314176    .3291465
          F2event |   -.059996   .1341081    -0.45   0.655    -.3233597    .2033677
          L0event |   .6092887   .2352056     2.59   0.010     .1473881    1.071189
          L1event |   .9038027   .2442243     3.70   0.000     .4241909    1.383414
          L2event |   1.047491   .2826629     3.71   0.000     .4923931    1.602589
          L3event |   .4175165   .2768998     1.51   0.132    -.1262639    .9612968
          L4event |   1.531036   .6986254     2.19   0.029     .1590645    2.903008
          L5event |   2.356691   .5865927     4.02   0.000     1.204731    3.508651
          L6event |  -.3848372   .9432163    -0.41   0.683    -2.237141    1.467466
          L7event |   -.503072   .8224809    -0.61   0.541    -2.118273    1.112129
                  |
 wahl_id#stadtbez |
        LTW13# 2  |  -7.085614   .8184088    -8.66   0.000    -8.692819    -5.47841
        LTW13# 3  |  -4.550842   .7386668    -6.16   0.000    -6.001448   -3.100236
        LTW13# 4  |  -5.141874    .750225    -6.85   0.000    -6.615178    -3.66857
        LTW13# 5  |  -8.116517   .8062445   -10.07   0.000    -9.699833   -6.533201
        LTW13# 6  |   -7.04143   .8139061    -8.65   0.000    -8.639792   -5.443068
        LTW13# 7  |  -6.740406   .7140441    -9.44   0.000    -8.142657   -5.338154
        LTW13# 8  |  -6.768832   1.078889    -6.27   0.000    -8.887571   -4.650093
        LTW13# 9  |  -6.174326   .7327378    -8.43   0.000    -7.613288   -4.735363
        LTW13#10  |  -8.448694   .7915746   -10.67   0.000     -10.0032   -6.894187
        LTW13#11  |   -6.29084   .7408208    -8.49   0.000    -7.745676   -4.836004
        LTW13#12  |  -6.062329   .6398957    -9.47   0.000    -7.318967   -4.805692
        LTW13#13  |  -7.274075   .6601793   -11.02   0.000    -8.570546   -5.977604
        LTW13#14  |  -6.714457    .874125    -7.68   0.000    -8.431078   -4.997836
        LTW13#15  |  -7.813706   .6386475   -12.23   0.000    -9.067893    -6.55952
        LTW13#16  |  -6.592856   .6236026   -10.57   0.000    -7.817497   -5.368215
        LTW13#17  |  -7.606405   .8701651    -8.74   0.000    -9.315249    -5.89756
        LTW13#18  |  -6.631743   .7841088    -8.46   0.000    -8.171588   -5.091897
        LTW13#19  |  -7.036547   .6058056   -11.62   0.000    -8.226238   -5.846857
        LTW13#20  |  -6.553093   .6156101   -10.64   0.000    -7.762038   -5.344148
        LTW13#21  |  -7.184122   .6590948   -10.90   0.000    -8.478463   -5.889781
        LTW13#22  |  -8.784728   .8402708   -10.45   0.000    -10.43487   -7.134591
        LTW13#23  |  -9.150598   .8486819   -10.78   0.000    -10.81725   -7.483943
        LTW13#24  |  -9.411173   .8522283   -11.04   0.000    -11.08479   -7.737553
        LTW13#25  |  -6.678772   .8018086    -8.33   0.000    -8.253377   -5.104167
        BTW13# 1  |   3.245323   .3420489     9.49   0.000     2.573602    3.917045
        BTW13# 2  |  -3.462194   .7968098    -4.35   0.000    -5.026982   -1.897406
        BTW13# 3  |  -.3272155   .7200125    -0.45   0.650    -1.741188    1.086757
        BTW13# 4  |  -2.217782    .779018    -2.85   0.005    -3.747631   -.6879342
        BTW13# 5  |  -4.890774   .7591577    -6.44   0.000     -6.38162   -3.399927
        BTW13# 6  |  -4.149322   .8557876    -4.85   0.000    -5.829931   -2.468712
        BTW13# 7  |  -4.327029   .7243862    -5.97   0.000     -5.74959   -2.904467
        BTW13# 8  |  -4.044711   1.095801    -3.69   0.000    -6.196662   -1.892759
        BTW13# 9  |  -3.984894   .7462992    -5.34   0.000    -5.450488   -2.519299
        BTW13#10  |  -6.978578   .7493649    -9.31   0.000    -8.450193   -5.506963
        BTW13#11  |  -4.134474   .8083645    -5.11   0.000    -5.721953   -2.546994
        BTW13#12  |  -2.969196   .7101518    -4.18   0.000    -4.363804   -1.574588
        BTW13#13  |  -4.737655   .7023734    -6.75   0.000    -6.116988   -3.358323
        BTW13#14  |  -4.744394   .8495209    -5.58   0.000    -6.412697   -3.076091
        BTW13#15  |  -6.067308   .6494756    -9.34   0.000    -7.342758   -4.791857
        BTW13#16  |  -4.989345   .6130297    -8.14   0.000    -6.193223   -3.785468
        BTW13#17  |  -5.044465    .820899    -6.15   0.000     -6.65656    -3.43237
        BTW13#18  |  -4.233493   .7836427    -5.40   0.000    -5.772423   -2.694562
        BTW13#19  |  -4.756327   .6268187    -7.59   0.000    -5.987284    -3.52537
        BTW13#20  |  -4.531678   .6341249    -7.15   0.000    -5.776983   -3.286373
        BTW13#21  |  -5.166718    .633612    -8.15   0.000    -6.411015    -3.92242
        BTW13#22  |  -7.567299   .8895807    -8.51   0.000    -9.314272   -5.820326
        BTW13#23  |  -7.263481   .8824007    -8.23   0.000    -8.996354   -5.530608
        BTW13#24  |  -7.961708   .7582231   -10.50   0.000    -9.450719   -6.472697
        BTW13#25  |  -4.305043    .783722    -5.49   0.000    -5.844129   -2.765957
        KOW14# 1  |  -7.415573   .6149969   -12.06   0.000    -8.623314   -6.207832
        KOW14# 2  |  -13.67543   .6530944   -20.94   0.000    -14.95799   -12.39288
        KOW14# 3  |  -12.58237   .5852077   -21.50   0.000    -13.73161   -11.43313
        KOW14# 4  |  -12.32885   .6614375   -18.64   0.000    -13.62779   -11.02991
        KOW14# 5  |  -14.01107   .6854661   -20.44   0.000     -15.3572   -12.66494
        KOW14# 6  |  -12.02765    .696352   -17.27   0.000    -13.39516   -10.66015
        KOW14# 7  |  -10.50398   .5886669   -17.84   0.000    -11.66002   -9.347949
        KOW14# 8  |  -11.44967   1.059357   -10.81   0.000    -13.53005   -9.369291
        KOW14# 9  |  -12.62358   .5778334   -21.85   0.000    -13.75833   -11.48882
        KOW14#10  |  -10.90686   .6514762   -16.74   0.000    -12.18624   -9.627479
        KOW14#11  |  -8.681594   .6370609   -13.63   0.000    -9.932664   -7.430523
        KOW14#12  |  -12.12251   .6373378   -19.02   0.000    -13.37413    -10.8709
        KOW14#13  |  -12.54461   .4998401   -25.10   0.000     -13.5262   -11.56301
        KOW14#14  |  -10.79117   .8478263   -12.73   0.000    -12.45615   -9.126196
        KOW14#15  |  -11.76869   .5831331   -20.18   0.000    -12.91385   -10.62352
        KOW14#16  |  -9.048872   .6003696   -15.07   0.000    -10.22789   -7.869856
        KOW14#17  |  -11.14484   .7240052   -15.39   0.000    -12.56665   -9.723027
        KOW14#18  |  -11.79607   .6392521   -18.45   0.000    -13.05144   -10.54069
        KOW14#19  |  -11.90231   .5388377   -22.09   0.000    -12.96049   -10.84413
        KOW14#20  |  -10.13291   .5486026   -18.47   0.000    -11.21026   -9.055552
        KOW14#21  |  -11.46413   .5750658   -19.94   0.000    -12.59345    -10.3348
        KOW14#22  |   -11.2762   .7868275   -14.33   0.000    -12.82138   -9.731011
        KOW14#23  |  -10.14511   .5744263   -17.66   0.000    -11.27317    -9.01704
        KOW14#24  |  -9.785815   .9796258    -9.99   0.000    -11.70962    -7.86201
        KOW14#25  |  -10.44341   .7636015   -13.68   0.000    -11.94299    -8.94384
        EUW14# 1  |  -6.828668   .6721106   -10.16   0.000    -8.148569   -5.508766
        EUW14# 2  |  -13.85051   .7195259   -19.25   0.000    -15.26352   -12.43749
        EUW14# 3  |  -12.14058   .6485281   -18.72   0.000    -13.41417   -10.86699
        EUW14# 4  |  -12.72209   .6898636   -18.44   0.000    -14.07686   -11.36733
        EUW14# 5  |  -13.82381   .7284457   -18.98   0.000    -15.25435   -12.39328
        EUW14# 6  |  -13.27778   .7887783   -16.83   0.000    -14.82679   -11.72876
        EUW14# 7  |  -11.55116    .659189   -17.52   0.000    -12.84569   -10.25664
        EUW14# 8  |  -11.69785   1.184265    -9.88   0.000    -14.02353   -9.372172
        EUW14# 9  |  -12.96693   .6650748   -19.50   0.000    -14.27301   -11.66084
        EUW14#10  |   -12.6079   .6589798   -19.13   0.000    -13.90202   -11.31379
        EUW14#11  |  -10.53463    .634323   -16.61   0.000    -11.78033   -9.288938
        EUW14#12  |  -11.83145    .584722   -20.23   0.000    -12.97974   -10.68316
        EUW14#13  |  -12.45434    .634252   -19.64   0.000    -13.69989   -11.20878
        EUW14#14  |  -11.15584   .8650517   -12.90   0.000    -12.85465   -9.457041
        EUW14#15  |  -12.41033   .6055233   -20.50   0.000    -13.59947    -11.2212
        EUW14#16  |  -9.899849   .6035191   -16.40   0.000    -11.08505   -8.714648
        EUW14#17  |  -12.34843   .6965651   -17.73   0.000    -13.71636   -10.98051
        EUW14#18  |  -12.36954   .6970268   -17.75   0.000    -13.73837   -11.00071
        EUW14#19  |  -12.30488   .5198862   -23.67   0.000    -13.32584   -11.28392
        EUW14#20  |   -11.0102   .6383153   -17.25   0.000    -12.26373   -9.756661
        EUW14#21  |  -12.82308   .6298775   -20.36   0.000    -14.06004   -11.58611
        EUW14#22  |  -13.91013   .8762897   -15.87   0.000      -15.631   -12.18926
        EUW14#23  |  -13.54085   .7165225   -18.90   0.000    -14.94796   -12.13373
        EUW14#24  |  -12.86432   .9442049   -13.62   0.000    -14.71857   -11.01008
        EUW14#25  |  -11.82586   .8129266   -14.55   0.000     -13.4223   -10.22942
        BTW17# 1  |   9.191889   .6662745    13.80   0.000     7.883448    10.50033
        BTW17# 2  |   4.085351   .8625716     4.74   0.000     2.391419    5.779284
        BTW17# 3  |   5.701919   .6582682     8.66   0.000     4.409201    6.994636
        BTW17# 4  |   2.650235   .8016594     3.31   0.001     1.075923    4.224546
        BTW17# 5  |   1.834481   .7360083     2.49   0.013     .3890959    3.279866
        BTW17# 6  |    1.13684    .582857     1.95   0.052    -.0077841    2.281464
        BTW17# 7  |   .5788738   .8054333     0.72   0.473    -1.002849    2.160597
        BTW17# 8  |   2.139201   1.237215     1.73   0.084    -.2904632    4.568865
        BTW17# 9  |   1.349059   .6438675     2.10   0.037     .0846213    2.613496
        BTW17#10  |  -1.236007   .7891478    -1.57   0.118    -2.785748    .3137345
        BTW17#11  |   .7456307   .8578431     0.87   0.385    -.9390155    2.430277
        BTW17#12  |    2.54055   .6977763     3.64   0.000     1.170246    3.910855
        BTW17#13  |   1.338399   .7844476     1.71   0.088    -.2021122     2.87891
        BTW17#14  |   1.762287   .9006758     1.96   0.051    -.0064751    3.531048
        BTW17#15  |  -1.001387   .6998935    -1.43   0.153    -2.375849    .3730752
        BTW17#16  |   .8796501   .6519599     1.35   0.178    -.4006794     2.15998
        BTW17#17  |   1.293271   .8137669     1.59   0.113    -.3048174     2.89136
        BTW17#18  |   1.554004    .684881     2.27   0.024     .2090233    2.898984
        BTW17#19  |   .3380686    .577273     0.59   0.558    -.7955895    1.471727
        BTW17#20  |  -.4873424   .6181598    -0.79   0.431    -1.701295    .7266098
        BTW17#21  |  -1.204808   .6354089    -1.90   0.058    -2.452634    .0430186
        BTW17#22  |  -2.881945   .7504753    -3.84   0.000    -4.355741    -1.40815
        BTW17#23  |  -1.325519   .7605675    -1.74   0.082    -2.819134    .1680958
        BTW17#24  |  -1.904536   .7642276    -2.49   0.013    -3.405339   -.4037339
        BTW17#25  |   1.200763   .7842513     1.53   0.126    -.3393628    2.740888
        LTW18# 1  |   6.126728   .6776562     9.04   0.000     4.795936     7.45752
        LTW18# 2  |   .6651354   .8376391     0.79   0.427    -.9798338    2.310105
        LTW18# 3  |   1.492835   .6078962     2.46   0.014     .2990387    2.686631
        LTW18# 4  |  -.7726158   .7486832    -1.03   0.302    -2.242892    .6976605
        LTW18# 5  |  -.6528434   .8320443    -0.78   0.433    -2.286825    .9811387
        LTW18# 6  |  -.9963077   .6054752    -1.65   0.100     -2.18535    .1927344
        LTW18# 7  |  -1.480965   .7594643    -1.95   0.052    -2.972413    .0104837
        LTW18# 8  |  -.6521501   1.147776    -0.57   0.570    -2.906171     1.60187
        LTW18# 9  |  -1.090666   .5591254    -1.95   0.052    -2.188686    .0073531
        LTW18#10  |  -2.748051   .7232716    -3.80   0.000    -4.168423   -1.327678
        LTW18#11  |  -1.605299   .7543261    -2.13   0.034    -3.086657    -.123941
        LTW18#12  |   -.873651   .6901141    -1.27   0.206    -2.228908    .4816063
        LTW18#13  |  -1.291708   .8178749    -1.58   0.115    -2.897864     .314448
        LTW18#14  |  -.7985782   .8118565    -0.98   0.326    -2.392915    .7957588
        LTW18#15  |   -3.03283   .5918355    -5.12   0.000    -4.195086   -1.870574
        LTW18#16  |  -2.165009    .627527    -3.45   0.001    -3.397357   -.9326611
        LTW18#17  |  -.9199548   .7402634    -1.24   0.214    -2.373696    .5337864
        LTW18#18  |  -1.626062   .6506294    -2.50   0.013    -2.903779   -.3483457
        LTW18#19  |  -2.083239   .5979099    -3.48   0.001    -3.257425   -.9090541
        LTW18#20  |  -2.018473   .6000635    -3.36   0.001    -3.196887   -.8400582
        LTW18#21  |  -3.024845   .6754529    -4.48   0.000     -4.35131    -1.69838
        LTW18#22  |  -4.276601   .7964513    -5.37   0.000    -5.840685   -2.712517
        LTW18#23  |  -2.967444    .857153    -3.46   0.001    -4.650735   -1.284153
        LTW18#24  |  -3.496421   .7669749    -4.56   0.000    -5.002619   -1.990223
        LTW18#25  |  -1.374612   .6983219    -1.97   0.049    -2.745987   -.0032357
        EUW19# 1  |    6.08795   .8730856     6.97   0.000      4.37337     7.80253
        EUW19# 2  |   1.851828   .7405412     2.50   0.013     .3975414    3.306115
        EUW19# 3  |   1.919278   .5721933     3.35   0.001     .7955958    3.042961
        EUW19# 4  |  -.2311061   .6489509    -0.36   0.722    -1.505526    1.043314
        EUW19# 5  |  -.0852593   .7407824    -0.12   0.908     -1.54002    1.369501
        EUW19# 6  |  -.9442181    .586438    -1.61   0.108    -2.095874    .2074383
        EUW19# 7  |  -1.064783   .7117145    -1.50   0.135    -2.462459    .3328938
        EUW19# 8  |   .4396353   .9841343     0.45   0.655    -1.493024    2.372294
        EUW19# 9  |    .036422    .513387     0.07   0.943    -.9717758     1.04462
        EUW19#10  |  -2.642419   .7467717    -3.54   0.000    -4.108941   -1.175896
        EUW19#11  |  -.5099746   .7644393    -0.67   0.505    -2.011193    .9912437
        EUW19#12  |   .1876892   .5600472     0.34   0.738    -.9121406    1.287519
        EUW19#13  |  -1.061692   .7124634    -1.49   0.137     -2.46084     .337455
        EUW19#14  |   -.980016   .7971286    -1.23   0.219     -2.54543    .5853981
        EUW19#15  |   -2.39005    .462192    -5.17   0.000     -3.29771   -1.482389
        EUW19#16  |  -1.587638   .5095611    -3.12   0.002    -2.588322   -.5869534
        EUW19#17  |  -.8665005   .6407196    -1.35   0.177    -2.124756    .3917551
        EUW19#18  |  -.3075265   .5008443    -0.61   0.539    -1.291093    .6760396
        EUW19#19  |  -1.496055   .4495636    -3.33   0.001    -2.378916   -.6131952
        EUW19#20  |  -2.135687   .6001777    -3.56   0.000    -3.314325   -.9570481
        EUW19#21  |  -1.961735   .5518561    -3.55   0.000    -3.045479   -.8779915
        EUW19#22  |   -3.80328   .6601851    -5.76   0.000    -5.099762   -2.506797
        EUW19#23  |  -2.508612   .6608182    -3.80   0.000    -3.806338   -1.210886
        EUW19#24  |   -3.73198   .6400643    -5.83   0.000    -4.988949   -2.475011
        EUW19#25  |    .106092   .6614319     0.16   0.873    -1.192839    1.405023
        KOW20# 1  |   4.805289   1.016054     4.73   0.000     2.809946    6.800633
        KOW20# 2  |          0  (omitted)
        KOW20# 3  |          0  (omitted)
        KOW20# 4  |          0  (omitted)
        KOW20# 5  |          0  (omitted)
        KOW20# 6  |          0  (omitted)
        KOW20# 7  |          0  (omitted)
        KOW20# 8  |          0  (omitted)
        KOW20# 9  |          0  (omitted)
        KOW20#10  |          0  (omitted)
        KOW20#11  |          0  (omitted)
        KOW20#12  |          0  (omitted)
        KOW20#13  |          0  (omitted)
        KOW20#14  |          0  (omitted)
        KOW20#15  |          0  (omitted)
        KOW20#16  |          0  (omitted)
        KOW20#17  |          0  (omitted)
        KOW20#18  |          0  (omitted)
        KOW20#19  |          0  (omitted)
        KOW20#20  |          0  (omitted)
        KOW20#21  |          0  (omitted)
        KOW20#22  |          0  (omitted)
        KOW20#23  |          0  (omitted)
        KOW20#24  |          0  (omitted)
        KOW20#25  |          0  (omitted)
                  |
        ln_ew_ges |   2.465914   1.449314     1.70   0.089    -.3802732    5.312101
         ew_biodt |    .388145   .0318355    12.19   0.000      .325626     .450664
        ew_dtmihi |  -.2303872   .0640128    -3.60   0.000    -.3560966   -.1046778
         ew_ledig |   .2111155   .0861744     2.45   0.015     .0418848    .3803462
       ew_married |   .2074512   .0858685     2.42   0.016     .0388213    .3760812
        wb_anteil |  -.2425344   .0240257   -10.09   0.000    -.2897165   -.1953522
          wb_ausl |   -.068913   .0156679    -4.40   0.000    -.0996819   -.0381441
         wb_18t24 |  -.0273058     .02959    -0.92   0.356    -.0854151    .0308035
         wb_25t34 |   .0526598   .0209193     2.52   0.012     .0115782    .0937415
         wb_35t44 |  -.0089296    .026774    -0.33   0.739    -.0615089    .0436496
         wb_45t59 |   -.038132   .0220631    -1.73   0.084    -.0814598    .0051958
          avg_dur |   .0459808    .025129     1.83   0.068    -.0033679    .0953295
          hh_kids |  -.0642629   .0448988    -1.43   0.153    -.1524358      .02391
mpreis_flats_rent |  -.0155286   .0251472    -0.62   0.537     -.064913    .0338559
            _cons |  -6.927711   12.26216    -0.56   0.572    -31.00833    17.15291
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 229

-------------------------------------------------------------------------------------------------
                                           Effect on mail-in turnout  Effect on mail-in turnout 
-------------------------------------------------------------------------------------------------
 Reassignment (#t-4#)                                -0.23                      -0.23           
                                                    (0.16)                     (0.16)           
 Reassignment (#t-3#)                                0.01                       0.01            
                                                    (0.15)                     (0.16)           
 Reassignment (#t-2#)                                -0.06                      -0.06           
                                                    (0.12)                     (0.14)           
 Reassignment (#t+0#)                               0.61**                     0.61**           
                                                    (0.22)                     (0.23)           
 Reassignment (#t+1#)                               0.90***                    0.90***          
                                                    (0.23)                     (0.25)           
 Reassignment (#t+2#)                               1.05***                    1.05***          
                                                    (0.26)                     (0.27)           
 R2                                                  0.96                       0.96            
 N                                                   4,666                      4,666           
 Precinct FE                                          X                          X              
 Election-District FE                                 X                          X              
 Election-District FE                                                                           
 Cluster Precinct                                     X                                         
 Number of Clusters                                   618                      200+618          
 TW Cluster District-Election + Precinct                                         X              
 WRC Precinct                                                                                   
-------------------------------------------------------------------------------------------------


              ----------------------------------------------------------------------
                                                         Effect on mail-in turnout 
              ----------------------------------------------------------------------
               Reassignment (#t-4#)                                -0.23           
                                                                  [0.146]          
               Reassignment (#t-3#)                                0.01            
                                                                  [0.963]          
               Reassignment (#t-2#)                                -0.06           
                                                                  [0.636]          
               Reassignment (#t+0#)                                0.61            
                                                                  [0.012]          
               Reassignment (#t+1#)                                0.90            
                                                                  [0.001]          
               Reassignment (#t+2#)                                1.05            
                                                                  [0.000]          
               R2                                                  0.96            
               N                                                   4,666           
               Precinct FE                                          X              
               Election-District FE                                 X              
               Election-District FE                                                
               Cluster Precinct                                                    
               Number of Clusters                                   618            
               TW Cluster District-Election + Precinct                             
               WRC Precinct                                         X              
              ----------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)
note: 1b.wahl_id#3.stadtbez omitted because of collinearity
note: 1b.wahl_id#6.stadtbez omitted because of collinearity
note: 1b.wahl_id#14.stadtbez omitted because of collinearity
note: 1b.wahl_id#17.stadtbez omitted because of collinearity
note: 4.wahl_id#11.stadtbez omitted because of collinearity
note: 6.wahl_id#7.stadtbez omitted because of collinearity
note: 6.wahl_id#13.stadtbez omitted because of collinearity
note: 6.wahl_id#16.stadtbez omitted because of collinearity
note: 6.wahl_id#19.stadtbez omitted because of collinearity
note: 6.wahl_id#20.stadtbez omitted because of collinearity
note: 6.wahl_id#22.stadtbez omitted because of collinearity
note: 6.wahl_id#24.stadtbez omitted because of collinearity
note: 7.wahl_id#2.stadtbez omitted because of collinearity
note: 7.wahl_id#8.stadtbez omitted because of collinearity
note: 7.wahl_id#10.stadtbez omitted because of collinearity
note: 7.wahl_id#12.stadtbez omitted because of collinearity
note: 7.wahl_id#18.stadtbez omitted because of collinearity
note: 7.wahl_id#21.stadtbez omitted because of collinearity
note: 8.wahl_id#4.stadtbez omitted because of collinearity
note: 8.wahl_id#5.stadtbez omitted because of collinearity
note: 8.wahl_id#9.stadtbez omitted because of collinearity
note: 8.wahl_id#15.stadtbez omitted because of collinearity
note: 8.wahl_id#23.stadtbez omitted because of collinearity
note: 8.wahl_id#25.stadtbez omitted because of collinearity

Linear regression, absorbing indicators             Number of obs     =  4,666
Absorbed variable: sb_new                           No. of categories =    618
                                                    F(24, 24)         =      .
                                                    Prob > F          =      .
                                                    R-squared         = 0.9616
                                                    Adj R-squared     = 0.9534
                                                    Root MSE          = 1.6417

                                   (Std. err. adjusted for 25 clusters in stadtbez)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3736253     0.22   0.825    -.6874878    .8547618
          F6event |   .2184141   .2893157     0.75   0.458    -.3787042    .8155324
          F5event |  -.4796951   .3414471    -1.40   0.173    -1.184407     .225017
          F4event |  -.2325114   .2035591    -1.14   0.265    -.6526368     .187614
          F3event |   .0074853   .1585316     0.05   0.963     -.319708    .3346785
          F2event |   -.059996   .1331631    -0.45   0.656    -.3348312    .2148392
          L0event |   .6092887   .2564532     2.38   0.026     .0799953    1.138582
          L1event |   .9038027   .2758395     3.28   0.003     .3344979    1.473107
          L2event |   1.047491   .3001478     3.49   0.002     .4280167    1.666966
          L3event |   .4175165   .3147236     1.33   0.197    -.2320411    1.067074
          L4event |   1.531036   .8634751     1.77   0.089    -.2510885    3.313161
          L5event |   2.356691   .4989348     4.72   0.000      1.32694    3.386442
          L6event |  -.3848372   .7961977    -0.48   0.633    -2.028109    1.258434
          L7event |   -.503072   .7622178    -0.66   0.516    -2.076212    1.070068
                  |
 wahl_id#stadtbez |
        LTW13# 2  |  -8.937443    .568044   -15.73   0.000    -10.10983   -7.765057
        LTW13# 3  |          0  (omitted)
        LTW13# 4  |  -5.141874   .6094971    -8.44   0.000    -6.399814   -3.883934
        LTW13# 5  |  -8.116517   .6531676   -12.43   0.000    -9.464589   -6.768445
        LTW13# 6  |          0  (omitted)
        LTW13# 7  |  -5.259441   .4033273   -13.04   0.000    -6.091868   -4.427014
        LTW13# 8  |  -7.208467    .614244   -11.74   0.000    -8.476205    -5.94073
        LTW13# 9  |  -6.174326   .6135183   -10.06   0.000    -7.440565   -4.908086
        LTW13#10  |  -5.806275   .5392233   -10.77   0.000    -6.919177   -4.693373
        LTW13#11  |   4.243792   .4246311     9.99   0.000     3.367397    5.120188
        LTW13#12  |  -6.250018   .4201466   -14.88   0.000    -7.117158   -5.382878
        LTW13#13  |  -5.982367    .385204   -15.53   0.000    -6.777389   -5.187345
        LTW13#14  |          0  (omitted)
        LTW13#15  |  -7.813706   .6021385   -12.98   0.000    -9.056459   -6.570953
        LTW13#16  |  -4.427847   .3880758   -11.41   0.000    -5.228796   -3.626898
        LTW13#17  |          0  (omitted)
        LTW13#18  |  -6.324216   .4187881   -15.10   0.000    -7.188552    -5.45988
        LTW13#19  |  -4.953308   .3762146   -13.17   0.000    -5.729777   -4.176839
        LTW13#20  |  -4.534621   .4186745   -10.83   0.000    -5.398722   -3.670519
        LTW13#21  |  -5.222387   .4235497   -12.33   0.000    -6.096551   -4.348223
        LTW13#22  |  -4.508128   .4648875    -9.70   0.000    -5.467609   -3.548647
        LTW13#23  |  -9.150598   .5332615   -17.16   0.000     -10.2512       -8.05
        LTW13#24  |  -5.914752   .4251272   -13.91   0.000    -6.792172   -5.037333
        LTW13#25  |  -6.678772   .5580624   -11.97   0.000    -7.830556   -5.526988
        BTW13# 1  |   3.245323   .0726377    44.68   0.000     3.095407     3.39524
        BTW13# 2  |  -5.314022   .5582068    -9.52   0.000    -6.466105    -4.16194
        BTW13# 3  |   4.223627   .0806275    52.38   0.000      4.05722    4.390034
        BTW13# 4  |  -2.217782   .5552709    -3.99   0.001    -3.363805    -1.07176
        BTW13# 5  |  -4.890774   .6131617    -7.98   0.000    -6.156277    -3.62527
        BTW13# 6  |   2.892109   .0654576    44.18   0.000     2.757011    3.027207
        BTW13# 7  |  -2.846064   .3867763    -7.36   0.000    -3.644331   -2.047797
        BTW13# 8  |  -4.484346   .5879853    -7.63   0.000    -5.697888   -3.270804
        BTW13# 9  |  -3.984894   .5762069    -6.92   0.000    -5.174127   -2.795661
        BTW13#10  |  -4.336159   .5206408    -8.33   0.000    -5.410709   -3.261609
        BTW13#11  |   6.400159   .4134908    15.48   0.000     5.546756    7.253562
        BTW13#12  |  -3.156885   .3990552    -7.91   0.000    -3.980495   -2.333276
        BTW13#13  |  -3.445947   .3737496    -9.22   0.000    -4.217329   -2.674566
        BTW13#14  |   1.970063   .0296634    66.41   0.000     1.908841    2.031286
        BTW13#15  |  -6.067308   .5871825   -10.33   0.000    -7.279193   -4.855423
        BTW13#16  |  -2.824336   .3800877    -7.43   0.000    -3.608799   -2.039874
        BTW13#17  |    2.56194   .0457934    55.95   0.000     2.467427    2.656453
        BTW13#18  |  -3.925966   .4107308    -9.56   0.000    -4.773673   -3.078259
        BTW13#19  |  -2.673088   .3713605    -7.20   0.000    -3.439538   -1.906637
        BTW13#20  |  -2.513205   .4117522    -6.10   0.000     -3.36302   -1.663391
        BTW13#21  |  -3.204982   .4042777    -7.93   0.000     -4.03937   -2.370594
        BTW13#22  |  -3.290699   .4577396    -7.19   0.000    -4.235427    -2.34597
        BTW13#23  |  -7.263481   .5150317   -14.10   0.000    -8.326454   -6.200508
        BTW13#24  |  -4.465287   .4189624   -10.66   0.000    -5.329983   -3.600591
        BTW13#25  |  -4.305043   .5430125    -7.93   0.000    -5.425766    -3.18432
        KOW14# 1  |  -7.415573   .5510188   -13.46   0.000     -8.55282   -6.278326
        KOW14# 2  |  -15.52726   .5424176   -28.63   0.000    -16.64676   -14.40777
        KOW14# 3  |  -8.031526   .5509977   -14.58   0.000    -9.168729   -6.894322
        KOW14# 4  |  -12.32885   .3982973   -30.95   0.000    -13.15089    -11.5068
        KOW14# 5  |  -14.01107   .3562469   -39.33   0.000    -14.74632   -13.27581
        KOW14# 6  |  -4.986224   .4860021   -10.26   0.000    -5.989283   -3.983165
        KOW14# 7  |  -9.023018   .7025301   -12.84   0.000    -10.47297   -7.573067
        KOW14# 8  |  -11.88931   .6264817   -18.98   0.000     -13.1823   -10.59631
        KOW14# 9  |  -12.62358   .4220254   -29.91   0.000    -13.49459   -11.75256
        KOW14#10  |   -8.26444    .543119   -15.22   0.000    -9.385382   -7.143497
        KOW14#11  |   1.853039   .5254395     3.53   0.002     .7685848    2.937492
        KOW14#12  |   -12.3102    .438137   -28.10   0.000    -13.21447   -11.40593
        KOW14#13  |   -11.2529   .6357416   -17.70   0.000    -12.56501   -9.940794
        KOW14#14  |  -4.076714   .4500376    -9.06   0.000    -5.005546   -3.147882
        KOW14#15  |  -11.76869   .3985636   -29.53   0.000    -12.59128   -10.94609
        KOW14#16  |  -6.883863   .6919144    -9.95   0.000    -8.311904   -5.455822
        KOW14#17  |  -3.538436   .5256415    -6.73   0.000    -4.623306   -2.453565
        KOW14#18  |  -11.48854   .4458165   -25.77   0.000    -12.40866   -10.56842
        KOW14#19  |  -9.819072   .6429043   -15.27   0.000    -11.14596   -8.492183
        KOW14#20  |  -8.114434   .6528257   -12.43   0.000      -9.4618   -6.767068
        KOW14#21  |  -9.502391   .4124116   -23.04   0.000    -10.35357   -8.651215
        KOW14#22  |  -6.999596   .6520755   -10.73   0.000    -8.345413   -5.653778
        KOW14#23  |  -10.14511   .3545528   -28.61   0.000    -10.87687   -9.413346
        KOW14#24  |  -6.289394   .7284312    -8.63   0.000    -7.792802   -4.785986
        KOW14#25  |  -10.44341   .3154122   -33.11   0.000    -11.09439   -9.792434
        EUW14# 1  |  -6.828668   .3256146   -20.97   0.000    -7.500703   -6.156632
        EUW14# 2  |  -15.70233   .3854559   -40.74   0.000    -16.49788   -14.90679
        EUW14# 3  |  -7.589742   .3201126   -23.71   0.000    -8.250422   -6.929062
        EUW14# 4  |  -12.72209   .5187758   -24.52   0.000    -13.79279   -11.65139
        EUW14# 5  |  -13.82381   .5256014   -26.30   0.000     -14.9086   -12.73903
        EUW14# 6  |  -6.236348   .3428358   -18.19   0.000    -6.943927    -5.52877
        EUW14# 7  |   -10.0702   .4528843   -22.24   0.000     -11.0049   -9.135489
        EUW14# 8  |  -12.13749   .2824624   -42.97   0.000    -12.72046   -11.55451
        EUW14# 9  |  -12.96693   .4874255   -26.60   0.000    -13.97293   -11.96093
        EUW14#10  |  -9.965485   .3072202   -32.44   0.000    -10.59956   -9.331414
        EUW14#11  |          0  (omitted)
        EUW14#12  |  -12.01914   .2345802   -51.24   0.000    -12.50329   -11.53499
        EUW14#13  |  -11.16263   .4518057   -24.71   0.000    -12.09511   -10.23015
        EUW14#14  |  -4.441387   .2867181   -15.49   0.000    -5.033144    -3.84963
        EUW14#15  |  -12.41033   .4740081   -26.18   0.000    -13.38864   -11.43203
        EUW14#16  |   -7.73484   .4731386   -16.35   0.000     -8.71135    -6.75833
        EUW14#17  |  -4.742028   .3320635   -14.28   0.000    -5.427374   -4.056683
        EUW14#18  |  -12.06201   .2486053   -48.52   0.000    -12.57511   -11.54892
        EUW14#19  |  -10.22164   .4428482   -23.08   0.000    -11.13564   -9.307649
        EUW14#20  |  -8.991723   .4564521   -19.70   0.000    -9.933794   -8.049652
        EUW14#21  |  -10.86134   .2648648   -41.01   0.000    -11.40799   -10.31469
        EUW14#22  |  -9.633531   .4729508   -20.37   0.000    -10.60965   -8.657408
        EUW14#23  |  -13.54085   .4378262   -30.93   0.000    -14.44447   -12.63722
        EUW14#24  |  -9.367902   .4798073   -19.52   0.000    -10.35818   -8.377629
        EUW14#25  |  -11.82586   .4352017   -27.17   0.000    -12.72408   -10.92765
        BTW17# 1  |   9.191889   .2801013    32.82   0.000     8.613788     9.76999
        BTW17# 2  |   2.233523   .3980592     5.61   0.000     1.411969    3.055077
        BTW17# 3  |   10.25276   .3113506    32.93   0.000     9.610165    10.89536
        BTW17# 4  |   2.650235     .47981     5.52   0.000     1.659956    3.640514
        BTW17# 5  |   1.834481   .5285381     3.47   0.002      .743632     2.92533
        BTW17# 6  |    8.17827   .2180413    37.51   0.000     7.728255    8.628285
        BTW17# 7  |   2.059839   .3215568     6.41   0.000     1.396178    2.723499
        BTW17# 8  |   1.699565   .5043385     3.37   0.003     .6586618    2.740469
        BTW17# 9  |   1.349059    .491942     2.74   0.011     .3337405    2.364377
        BTW17#10  |   1.406412   .4050936     3.47   0.002     .5703399    2.242484
        BTW17#11  |   11.28026   .4300711    26.23   0.000     10.39264    12.16789
        BTW17#12  |   2.352861    .306315     7.68   0.000     1.720658    2.985064
        BTW17#13  |   2.630107   .3638537     7.23   0.000      1.87915    3.381064
        BTW17#14  |   8.476744    .251032    33.77   0.000     7.958639    8.994848
        BTW17#15  |  -1.001387   .4673037    -2.14   0.042    -1.965854   -.0369196
        BTW17#16  |   3.044659   .3536762     8.61   0.000     2.314707    3.774611
        BTW17#17  |   8.899676   .1967324    45.24   0.000      8.49364    9.305712
        BTW17#18  |    1.86153   .3275319     5.68   0.000     1.185538    2.537523
        BTW17#19  |   2.421308   .3506382     6.91   0.000     1.697626     3.14499
        BTW17#20  |    1.53113   .3542826     4.32   0.000     .7999268    2.262333
        BTW17#21  |   .7569278   .2872104     2.64   0.014     .1641546    1.349701
        BTW17#22  |   1.394655    .370293     3.77   0.001     .6304078    2.158902
        BTW17#23  |  -1.325519   .4939237    -2.68   0.013    -2.344927   -.3061106
        BTW17#24  |   1.591884   .3642746     4.37   0.000     .8400587     2.34371
        BTW17#25  |   1.200763   .5246265     2.29   0.031     .1179868    2.283538
        LTW18# 1  |   6.126728   .3190341    19.20   0.000     5.468274    6.785182
        LTW18# 2  |  -1.186693   .3879876    -3.06   0.005     -1.98746   -.3859257
        LTW18# 3  |   6.043678   .3313664    18.24   0.000     5.359771    6.727584
        LTW18# 4  |  -.7726158   .6249667    -1.24   0.228    -2.062484    .5172521
        LTW18# 5  |  -.6528434   .6189004    -1.05   0.302    -1.930191    .6245043
        LTW18# 6  |   6.045123   .2736028    22.09   0.000     5.480434    6.609811
        LTW18# 7  |          0  (omitted)
        LTW18# 8  |  -1.091785   .4911937    -2.22   0.036    -2.105559   -.0780114
        LTW18# 9  |  -1.090666   .6220417    -1.75   0.092    -2.374497    .1931645
        LTW18#10  |  -.1056318   .5472659    -0.19   0.849    -1.235133    1.023869
        LTW18#11  |   8.929333    .535485    16.68   0.000     7.824147    10.03452
        LTW18#12  |   -1.06134   .4201767    -2.53   0.019    -1.928542   -.1941382
        LTW18#13  |          0  (omitted)
        LTW18#14  |   5.915879   .3579607    16.53   0.000     5.177084    6.654674
        LTW18#15  |   -3.03283   .6836583    -4.44   0.000    -4.443832   -1.621829
        LTW18#16  |          0  (omitted)
        LTW18#17  |    6.68645   .2880827    23.21   0.000     6.091876    7.281023
        LTW18#18  |  -1.318536   .4673968    -2.82   0.009    -2.283195    -.353876
        LTW18#19  |          0  (omitted)
        LTW18#20  |          0  (omitted)
        LTW18#21  |   -1.06311   .5048113    -2.11   0.046    -2.104989   -.0212303
        LTW18#22  |          0  (omitted)
        LTW18#23  |  -2.967444   .7377172    -4.02   0.000    -4.490017   -1.444871
        LTW18#24  |          0  (omitted)
        LTW18#25  |  -1.374612   .7563428    -1.82   0.082    -2.935626    .1864032
        EUW19# 1  |    6.08795   .6351667     9.58   0.000      4.77703     7.39887
        EUW19# 2  |          0  (omitted)
        EUW19# 3  |   6.470121   .4756292    13.60   0.000      5.48847    7.451771
        EUW19# 4  |  -.2311061   .3522654    -0.66   0.518     -.958146    .4959339
        EUW19# 5  |  -.0852593   .3881323    -0.22   0.828    -.8863251    .7158065
        EUW19# 6  |   6.097212   .5124263    11.90   0.000     5.039616    7.154808
        EUW19# 7  |   .4161821   .5167147     0.81   0.428    -.6502646    1.482629
        EUW19# 8  |          0  (omitted)
        EUW19# 9  |    .036422   .3786021     0.10   0.924    -.7449743    .8178183
        EUW19#10  |          0  (omitted)
        EUW19#11  |   10.02466   .3483801    28.78   0.000     9.305637    10.74368
        EUW19#12  |          0  (omitted)
        EUW19#13  |   .2300158   .5174605     0.44   0.661    -.8379702    1.298002
        EUW19#14  |   5.734441   .5701982    10.06   0.000      4.55761    6.911272
        EUW19#15  |   -2.39005   .3353653    -7.13   0.000     -3.08221    -1.69789
        EUW19#16  |    .577371   .5819453     0.99   0.331    -.6237051    1.778447
        EUW19#17  |   6.739904   .4807143    14.02   0.000     5.747759     7.73205
        EUW19#18  |          0  (omitted)
        EUW19#19  |   .5871839   .5171918     1.14   0.267    -.4802476    1.654615
        EUW19#20  |  -.1172142   .5210827    -0.22   0.824    -1.192676    .9582476
        EUW19#21  |          0  (omitted)
        EUW19#22  |   .4733209   .5267685     0.90   0.378    -.6138759    1.560518
        EUW19#23  |  -2.508612    .415374    -6.04   0.000    -3.365902   -1.651322
        EUW19#24  |   -.235559   .5693573    -0.41   0.683    -1.410655    .9395368
        EUW19#25  |    .106092   .4220649     0.25   0.804     -.765007    .9771911
        KOW20# 1  |   4.805289   .7873428     6.10   0.000     3.180294    6.430285
        KOW20# 2  |  -1.851828   .4464284    -4.15   0.000    -2.773211   -.9304453
        KOW20# 3  |   4.550842   .6474399     7.03   0.000     3.214592    5.887093
        KOW20# 4  |          0  (omitted)
        KOW20# 5  |          0  (omitted)
        KOW20# 6  |    7.04143   .6660779    10.57   0.000     5.666713    8.416148
        KOW20# 7  |   1.480965   .7523413     1.97   0.061    -.0717915    3.033721
        KOW20# 8  |  -.4396353   .5456771    -0.81   0.428    -1.565858     .686587
        KOW20# 9  |          0  (omitted)
        KOW20#10  |   2.642419   .4119539     6.41   0.000     1.792188     3.49265
        KOW20#11  |   10.53463   .5877308    17.92   0.000     9.321615    11.74765
        KOW20#12  |  -.1876892   .3617414    -0.52   0.609    -.9342866    .5589083
        KOW20#13  |   1.291708   .7115548     1.82   0.082    -.1768688    2.760285
        KOW20#14  |   6.714457   .6568194    10.22   0.000     5.358849    8.070066
        KOW20#15  |          0  (omitted)
        KOW20#16  |   2.165009   .8103555     2.67   0.013     .4925172      3.8375
        KOW20#17  |   7.606405   .6189806    12.29   0.000     6.328892    8.883918
        KOW20#18  |   .3075265   .4254456     0.72   0.477      -.57055    1.185603
        KOW20#19  |   2.083239   .7305337     2.85   0.009     .5754919    3.590987
        KOW20#20  |   2.018473   .7061397     2.86   0.009     .5610718    3.475873
        KOW20#21  |   1.961735   .3553993     5.52   0.000     1.228227    2.695244
        KOW20#22  |   4.276601    .717854     5.96   0.000     2.795023    5.758178
        KOW20#23  |          0  (omitted)
        KOW20#24  |   3.496421   .7882108     4.44   0.000     1.869634    5.123208
        KOW20#25  |          0  (omitted)
                  |
        ln_ew_ges |   2.465914   1.237925     1.99   0.058    -.0890369    5.020864
         ew_biodt |    .388145    .028197    13.77   0.000     .3299492    .4463408
        ew_dtmihi |  -.2303872   .0923005    -2.50   0.020    -.4208861   -.0398883
         ew_ledig |   .2111155   .0701487     3.01   0.006     .0663357    .3558953
       ew_married |   .2074512   .0614427     3.38   0.002     .0806397    .3342628
        wb_anteil |  -.2425344   .0336902    -7.20   0.000    -.3120676   -.1730011
          wb_ausl |   -.068913   .0170979    -4.03   0.000    -.1042014   -.0336246
         wb_18t24 |  -.0273058   .0289238    -0.94   0.355    -.0870016      .03239
         wb_25t34 |   .0526598   .0205792     2.56   0.017     .0101863    .0951333
         wb_35t44 |  -.0089296   .0332416    -0.27   0.791     -.077537    .0596777
         wb_45t59 |   -.038132   .0222359    -1.71   0.099    -.0840246    .0077606
          avg_dur |   .0459808   .0272879     1.69   0.105    -.0103387    .1023002
          hh_kids |  -.0642629   .0464169    -1.38   0.179    -.1600627    .0315369
mpreis_flats_rent |  -.0155286   .0238339    -0.65   0.521    -.0647193    .0336622
            _cons |  -9.091297   12.64818    -0.72   0.479    -35.19586    17.01326
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 229

-------------------------------------------------------------------------------------------------
                                           Effect on mail-in turnout  Effect on mail-in turnout 
-------------------------------------------------------------------------------------------------
 Reassignment (#t-4#)                                -0.23                      -0.23           
                                                    (0.16)                     (0.16)           
 Reassignment (#t-3#)                                0.01                       0.01            
                                                    (0.15)                     (0.16)           
 Reassignment (#t-2#)                                -0.06                      -0.06           
                                                    (0.12)                     (0.14)           
 Reassignment (#t+0#)                               0.61**                     0.61**           
                                                    (0.22)                     (0.23)           
 Reassignment (#t+1#)                               0.90***                    0.90***          
                                                    (0.23)                     (0.25)           
 Reassignment (#t+2#)                               1.05***                    1.05***          
                                                    (0.26)                     (0.27)           
 R2                                                  0.96                       0.96            
 N                                                   4,666                      4,666           
 Precinct FE                                          X                          X              
 Election-District FE                                 X                          X              
 Election-District FE                                                                           
 Cluster Precinct                                     X                                         
 Number of Clusters                                   618                      200+618          
 TW Cluster District-Election + Precinct                                         X              
 WRC Precinct                                                                                   
 WRC District                                                                                   
-------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                           Effect on mail-in turnout  Effect on mail-in turnout 
-------------------------------------------------------------------------------------------------
 Reassignment (#t-4#)                                -0.23                      -0.23           
                                                    [0.146]                    [0.233]          
 Reassignment (#t-3#)                                0.01                       0.01            
                                                    [0.963]                    [0.963]          
 Reassignment (#t-2#)                                -0.06                      -0.06           
                                                    [0.636]                    [0.637]          
 Reassignment (#t+0#)                                0.61                       0.61            
                                                    [0.012]                    [0.016]          
 Reassignment (#t+1#)                                0.90                       0.90            
                                                    [0.001]                    [0.002]          
 Reassignment (#t+2#)                                1.05                       1.05            
                                                    [0.000]                    [0.000]          
 R2                                                  0.96                       0.96            
 N                                                   4,666                      4,666           
 Precinct FE                                          X                          X              
 Election-District FE                                 X                          X              
 Election-District FE                                                                           
 Cluster Precinct                                                                               
 Number of Clusters                                   618                        25             
 TW Cluster District-Election + Precinct                                                        
 WRC Precinct                                         X                                         
 WRC District                                                                    X              
-------------------------------------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)

Linear regression, absorbing indicators             Number of obs     =  4,666
Absorbed variable: sb_new                           No. of categories =    618
                                                    F(24, 24)         =      .
                                                    Prob > F          =      .
                                                    R-squared         = 0.9457
                                                    Adj R-squared     = 0.9369
                                                    Root MSE          = 1.9520

                                   (Std. err. adjusted for 25 clusters in stadtbez)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .276988   .3599474     0.77   0.449    -.4659069    1.019883
          F6event |   .3148414   .3598998     0.87   0.390    -.4279552    1.057638
          F5event |  -.3360842   .2949768    -1.14   0.266    -.9448864     .272718
          F4event |  -.1089699   .1636632    -0.67   0.512    -.4467541    .2288143
          F3event |  -.1143589   .2276705    -0.50   0.620    -.5842477      .35553
          F2event |  -.1662226   .1973949    -0.84   0.408    -.5736257    .2411805
          L0event |   .5381031   .2870466     1.87   0.073    -.0543319    1.130538
          L1event |   .8706087    .320871     2.71   0.012     .2083635    1.532854
          L2event |   .9679545   .3242861     2.98   0.006     .2986609    1.637248
          L3event |   .0769893   .3355204     0.23   0.820    -.6154908    .7694694
          L4event |   1.629122   .8831602     1.84   0.077    -.1936308    3.451875
          L5event |   .9910967   .6855583     1.45   0.161     -.423826    2.406019
          L6event |   .0056946   .4727851     0.01   0.990    -.9700859     .981475
          L7event |   -.400396   .9517052    -0.42   0.678    -2.364619    1.563827
                  |
          wahl_id |
           BTW13  |   2.483428   .2047084    12.13   0.000     2.060931    2.905925
           KOW14  |  -4.021457   .5932849    -6.78   0.000    -5.245937   -2.796977
           EUW14  |  -5.460013   .3883185   -14.06   0.000    -6.261463   -4.658563
           BTW17  |   7.752056   .3647922    21.25   0.000     6.999162     8.50495
           LTW18  |   5.185047    .344539    15.05   0.000     4.473953     5.89614
           EUW19  |   5.470835   .4391113    12.46   0.000     4.564554    6.377116
           KOW20  |   7.020551   .6220049    11.29   0.000     5.736796    8.304306
                  |
        ln_ew_ges |   2.396505    1.28157     1.87   0.074    -.2485266    5.041536
         ew_biodt |   .4381682   .0301397    14.54   0.000      .375963    .5003735
        ew_dtmihi |  -.2278822   .0782898    -2.91   0.008    -.3894644   -.0662999
         ew_ledig |     .16008   .0656449     2.44   0.023     .0245955    .2955644
       ew_married |   .1907753      .0644     2.96   0.007     .0578602    .3236904
        wb_anteil |  -.2980442   .0371043    -8.03   0.000    -.3746237   -.2214647
          wb_ausl |   -.056026   .0205319    -2.73   0.012    -.0984017   -.0136503
         wb_18t24 |  -.0102823   .0368803    -0.28   0.783    -.0863994    .0658348
         wb_25t34 |   .0315455   .0167226     1.89   0.071    -.0029683    .0660594
         wb_35t44 |  -.0088774   .0228348    -0.39   0.701    -.0560061    .0382514
         wb_45t59 |   -.027485   .0194701    -1.41   0.171    -.0676693    .0126993
          avg_dur |   .0609851   .0309633     1.97   0.061      -.00292    .1248901
          hh_kids |  -.0405334   .0409862    -0.99   0.333    -.1251248     .044058
mpreis_flats_rent |   .0523958   .0402552     1.30   0.205    -.0306867    .1354784
            _cons |  -11.41906   13.14024    -0.87   0.393    -38.53919    15.70107
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 37

-------------------------------------------------------------------------------------------------
                                           Effect on mail-in turnout  Effect on mail-in turnout 
-------------------------------------------------------------------------------------------------
 Reassignment (#t-4#)                                -0.23                      -0.23           
                                                    (0.16)                     (0.16)           
 Reassignment (#t-3#)                                0.01                       0.01            
                                                    (0.15)                     (0.16)           
 Reassignment (#t-2#)                                -0.06                      -0.06           
                                                    (0.12)                     (0.14)           
 Reassignment (#t+0#)                               0.61**                     0.61**           
                                                    (0.22)                     (0.23)           
 Reassignment (#t+1#)                               0.90***                    0.90***          
                                                    (0.23)                     (0.25)           
 Reassignment (#t+2#)                               1.05***                    1.05***          
                                                    (0.26)                     (0.27)           
 R2                                                  0.96                       0.96            
 N                                                   4,666                      4,666           
 Precinct FE                                          X                          X              
 Election-District FE                                 X                          X              
 Election-District FE                                                                           
 Cluster Precinct                                     X                                         
 Number of Clusters                                   618                      200+618          
 TW Cluster District-Election + Precinct                                         X              
 WRC Precinct                                                                                   
 WRC District                                                                                   
 Election FE                                                                                    
-------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                           Effect on mail-in turnout  Effect on mail-in turnout 
-------------------------------------------------------------------------------------------------
 Reassignment (#t-4#)                                -0.23                      -0.23           
                                                    [0.146]                    [0.233]          
 Reassignment (#t-3#)                                0.01                       0.01            
                                                    [0.963]                    [0.963]          
 Reassignment (#t-2#)                                -0.06                      -0.06           
                                                    [0.636]                    [0.637]          
 Reassignment (#t+0#)                                0.61                       0.61            
                                                    [0.012]                    [0.016]          
 Reassignment (#t+1#)                                0.90                       0.90            
                                                    [0.001]                    [0.002]          
 Reassignment (#t+2#)                                1.05                       1.05            
                                                    [0.000]                    [0.000]          
 R2                                                  0.96                       0.96            
 N                                                   4,666                      4,666           
 Precinct FE                                          X                          X              
 Election-District FE                                 X                          X              
 Election-District FE                                                                           
 Cluster Precinct                                                                               
 Number of Clusters                                   618                        25             
 TW Cluster District-Election + Precinct                                                        
 WRC Precinct                                         X                                         
 WRC District                                                                    X              
 Election FE                                                                                    
-------------------------------------------------------------------------------------------------


              ----------------------------------------------------------------------
                                                         Effect on mail-in turnout 
              ----------------------------------------------------------------------
               Reassignment (#t-4#)                                -0.11           
                                                                  [0.486]          
               Reassignment (#t-3#)                                -0.11           
                                                                  [0.603]          
               Reassignment (#t-2#)                                -0.17           
                                                                  [0.400]          
               Reassignment (#t+0#)                                0.54            
                                                                  [0.063]          
               Reassignment (#t+1#)                                0.87            
                                                                  [0.016]          
               Reassignment (#t+2#)                                0.97            
                                                                  [0.012]          
               R2                                                  0.95            
               N                                                   4,666           
               Precinct FE                                          X              
               Election-District FE                                                
               Election-District FE                                                
               Cluster Precinct                                                    
               Number of Clusters                                   25             
               TW Cluster District-Election + Precinct                             
               WRC Precinct                                                        
               WRC District                                         X              
               Election FE                                          X              
              ----------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                           Effect on mail-in turnout  Effect on mail-in turnout 
-------------------------------------------------------------------------------------------------
 Reassignment (#t-4#)                                -0.23                      -0.23           
                                                    (0.16)                     (0.16)           
 Reassignment (#t-3#)                                0.01                       0.01            
                                                    (0.15)                     (0.16)           
 Reassignment (#t-2#)                                -0.06                      -0.06           
                                                    (0.12)                     (0.14)           
 Reassignment (#t+0#)                               0.61**                     0.61**           
                                                    (0.22)                     (0.23)           
 Reassignment (#t+1#)                               0.90***                    0.90***          
                                                    (0.23)                     (0.25)           
 Reassignment (#t+2#)                               1.05***                    1.05***          
                                                    (0.26)                     (0.27)           
 R2                                                  0.96                       0.96            
 N                                                   4,666                      4,666           
 Precinct FE                                          X                          X              
 Election-District FE                                 X                          X              
 Election-District FE                                                                           
 Cluster Precinct                                     X                                         
 Number of Clusters                                   618                      200+618          
 TW Cluster District-Election + Precinct                                         X              
 WRC Precinct                                                                                   
 WRC District                                                                                   
 Election FE                                                                                    
-------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                           Effect on mail-in turnout  Effect on mail-in turnout 
-------------------------------------------------------------------------------------------------
 Reassignment (#t-4#)                                -0.23                      -0.23           
                                                    [0.146]                    [0.233]          
 Reassignment (#t-3#)                                0.01                       0.01            
                                                    [0.963]                    [0.963]          
 Reassignment (#t-2#)                                -0.06                      -0.06           
                                                    [0.636]                    [0.637]          
 Reassignment (#t+0#)                                0.61                       0.61            
                                                    [0.012]                    [0.016]          
 Reassignment (#t+1#)                                0.90                       0.90            
                                                    [0.001]                    [0.002]          
 Reassignment (#t+2#)                                1.05                       1.05            
                                                    [0.000]                    [0.000]          
 R2                                                  0.96                       0.96            
 N                                                   4,666                      4,666           
 Precinct FE                                          X                          X              
 Election-District FE                                 X                          X              
 Election-District FE                                                                           
 Cluster Precinct                                                                               
 Number of Clusters                                   618                        25             
 TW Cluster District-Election + Precinct                                                        
 WRC Precinct                                         X                                         
 WRC District                                                                    X              
 Election FE                                                                                    
-------------------------------------------------------------------------------------------------


              ----------------------------------------------------------------------
                                                         Effect on mail-in turnout 
              ----------------------------------------------------------------------
               Reassignment (#t-4#)                                -0.11           
                                                                  [0.486]          
               Reassignment (#t-3#)                                -0.11           
                                                                  [0.603]          
               Reassignment (#t-2#)                                -0.17           
                                                                  [0.400]          
               Reassignment (#t+0#)                                0.54            
                                                                  [0.063]          
               Reassignment (#t+1#)                                0.87            
                                                                  [0.016]          
               Reassignment (#t+2#)                                0.97            
                                                                  [0.012]          
               R2                                                  0.95            
               N                                                   4,666           
               Precinct FE                                          X              
               Election-District FE                                                
               Election-District FE                                                
               Cluster Precinct                                                    
               Number of Clusters                                   25             
               TW Cluster District-Election + Precinct                             
               WRC Precinct                                                        
               WRC District                                         X              
               Election FE                                          X              
              ----------------------------------------------------------------------

(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4395
Number of clusters (sb_new)  =        618         Root MSE        =     1.6245

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3259399    -0.25   0.800    -.7224958    .5576764
          F6event |   .2549675   .2761707     0.92   0.356    -.2873811    .7973161
          F5event |  -.2369689   .2619342    -0.90   0.366    -.7513594    .2774217
          F4event |  -.2192898   .1694872    -1.29   0.196    -.5521315     .113552
          F3event |  -.0490231   .1579758    -0.31   0.756    -.3592586    .2612124
          F2event |  -.0537065   .1322374    -0.41   0.685    -.3133964    .2059834
          L0event |  -.3892706   .1633916    -2.38   0.018    -.7101417   -.0683996
          L1event |    .011072    .200952     0.06   0.956    -.3835608    .4057048
          L2event |   .2959665    .224252     1.32   0.187    -.1444233    .7363563
          L3event |   .1243464   .2332215     0.53   0.594    -.3336578    .5823505
          L4event |   .6528057   .6207241     1.05   0.293    -.5661824    1.871794
          L5event |   1.807978   .7132727     2.53   0.011      .407241    3.208714
          L6event |   .6923315   .7952045     0.87   0.384     -.869304    2.253967
          L7event |   .4397263   1.099927     0.40   0.689    -1.720328    2.599781
        ln_ew_ges |   1.678109   1.068642     1.57   0.117    -.4205078    3.776725
         ew_biodt |    .761748   .0314252    24.24   0.000     .7000348    .8234612
        ew_dtmihi |  -.1673742   .0522282    -3.20   0.001    -.2699407   -.0648076
         ew_ledig |   .4238555   .0699086     6.06   0.000     .2865679    .5611432
       ew_married |   .6387186   .0688582     9.28   0.000     .5034938    .7739433
        wb_anteil |  -.5320422   .0239517   -22.21   0.000     -.579079   -.4850055
          wb_ausl |  -.0535363   .0175921    -3.04   0.002     -.088084   -.0189885
         wb_18t24 |  -.0438039    .025903    -1.69   0.091    -.0946727    .0070648
         wb_25t34 |  -.0197863   .0166674    -1.19   0.236     -.052518    .0129454
         wb_35t44 |  -.0028286   .0208991    -0.14   0.892    -.0438706    .0382134
         wb_45t59 |  -.0241236   .0196706    -1.23   0.221    -.0627531    .0145059
          avg_dur |   .0170995    .022054     0.78   0.438    -.0262106    .0604096
          hh_kids |  -.1148866    .035755    -3.21   0.001    -.1851028   -.0446704
mpreis_flats_rent |   .0148646   .0236111     0.63   0.529    -.0315033    .0612326
            _cons |   -.366715   10.03193    -0.04   0.971    -20.06759    19.33416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: matrix in addrows option has varying size rows:
   `"R2 "',`"0.99"' \ `"N"',`"4,666"'\Precinct FE, X \ Election-District FE, X \ Election-District
>  FE, \ Cluster Precinct, X \ Number of Clusters, 618
warning: no existing table found for merge or append

                        -------------------------------------------------
                                                Effect on total turnout 
                        -------------------------------------------------
                         Reassignment (#t-4#)            -0.22          
                                                        (0.17)          
                         Reassignment (#t-3#)            -0.05          
                                                        (0.16)          
                         Reassignment (#t-2#)            -0.05          
                                                        (0.13)          
                         Reassignment (#t+0#)           -0.39*          
                                                        (0.16)          
                         Reassignment (#t+1#)            0.01           
                                                        (0.20)          
                         Reassignment (#t+2#)            0.30           
                                                        (0.22)          
                         R2                              0.99           
                         N                               4,666          
                         Precinct FE                      X             
                         Election-District FE             X             
                         Election-District FE                           
                         Cluster Precinct                 X             
                         Number of Clusters               618           
                        -------------------------------------------------

(MWFE estimator converged in 8 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller appl
> ied.
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    199) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
Number of clusters (sb_new)  =        618         Within R-sq.    =     0.4395
Number of clusters (stadtbez#wahl_id) =        200Root MSE        =     1.6247

                   (Std. err. adjusted for 200 clusters in sb_new stadtbez#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3073735    -0.27   0.789    -.6885368    .5237174
          F6event |   .2549675   .2416582     1.06   0.293     -.221572     .731507
          F5event |  -.2369689   .2562251    -0.92   0.356    -.7422336    .2682958
          F4event |  -.2192898   .1690081    -1.30   0.196    -.5525665    .1139869
          F3event |  -.0490231   .1561154    -0.31   0.754    -.3568759    .2588297
          F2event |  -.0537065   .1361043    -0.39   0.694    -.3220982    .2146852
          L0event |  -.3892706   .1856807    -2.10   0.037    -.7554248   -.0231164
          L1event |    .011072   .2108976     0.05   0.958     -.404809     .426953
          L2event |   .2959665   .2065919     1.43   0.154    -.1114237    .7033567
          L3event |   .1243464   .2586939     0.48   0.631    -.3857867    .6344794
          L4event |   .6528057    .681893     0.96   0.340    -.6918576    1.997469
          L5event |   1.807978    .562151     3.22   0.002     .6994403    2.916515
          L6event |   .6923315    .781572     0.89   0.377    -.8488946    2.233558
          L7event |   .4397263   1.077887     0.41   0.684     -1.68582    2.565273
        ln_ew_ges |   1.678109   1.120318     1.50   0.136     -.531109    3.887326
         ew_biodt |    .761748   .0366389    20.79   0.000     .6894977    .8339983
        ew_dtmihi |  -.1673742   .0601873    -2.78   0.006    -.2860609   -.0486874
         ew_ledig |   .4238555   .0735406     5.76   0.000     .2788367    .5688744
       ew_married |   .6387186   .0703939     9.07   0.000     .4999048    .7775323
        wb_anteil |  -.5320422   .0308026   -17.27   0.000    -.5927837   -.4713008
          wb_ausl |  -.0535363   .0211062    -2.54   0.012    -.0951567   -.0119158
         wb_18t24 |  -.0438039   .0254634    -1.72   0.087    -.0940166    .0064088
         wb_25t34 |  -.0197863   .0164682    -1.20   0.231     -.052261    .0126883
         wb_35t44 |  -.0028286   .0238838    -0.12   0.906    -.0499264    .0442692
         wb_45t59 |  -.0241236   .0222659    -1.08   0.280     -.068031    .0197838
          avg_dur |   .0170995   .0228375     0.75   0.455     -.027935     .062134
          hh_kids |  -.1148866   .0400645    -2.87   0.005    -.1938921   -.0358812
mpreis_flats_rent |   .0148646   .0249149     0.60   0.551    -.0342665    .0639958
            _cons |   -.366715    10.4395    -0.04   0.972    -20.95296    20.21953
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200         200           0    *|
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

  ---------------------------------------------------------------------------------------------
                                             Effect on total turnout  Effect on total turnout 
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                               -0.22                    -0.22          
                                                     (0.17)                   (0.17)          
   Reassignment (#t-3#)                               -0.05                    -0.05          
                                                     (0.16)                   (0.16)          
   Reassignment (#t-2#)                               -0.05                    -0.05          
                                                     (0.13)                   (0.14)          
   Reassignment (#t+0#)                              -0.39*                   -0.39*          
                                                     (0.16)                   (0.19)          
   Reassignment (#t+1#)                               0.01                     0.01           
                                                     (0.20)                   (0.21)          
   Reassignment (#t+2#)                               0.30                     0.30           
                                                     (0.22)                   (0.21)          
   R2                                                 0.99                     0.99           
   N                                                  4,666                    4,666          
   Precinct FE                                         X                        X             
   Election-District FE                                X                        X             
   Election-District FE                                                                       
   Cluster Precinct                                    X                                      
   Number of Clusters                                  618                    200+618         
   TW Cluster District-Election + Precinct                                      X             
  ---------------------------------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)
note: 8.wahl_id#2.stadtbez omitted because of collinearity.
note: 8.wahl_id#3.stadtbez omitted because of collinearity.
note: 8.wahl_id#4.stadtbez omitted because of collinearity.
note: 8.wahl_id#5.stadtbez omitted because of collinearity.
note: 8.wahl_id#6.stadtbez omitted because of collinearity.
note: 8.wahl_id#7.stadtbez omitted because of collinearity.
note: 8.wahl_id#8.stadtbez omitted because of collinearity.
note: 8.wahl_id#9.stadtbez omitted because of collinearity.
note: 8.wahl_id#10.stadtbez omitted because of collinearity.
note: 8.wahl_id#11.stadtbez omitted because of collinearity.
note: 8.wahl_id#12.stadtbez omitted because of collinearity.
note: 8.wahl_id#13.stadtbez omitted because of collinearity.
note: 8.wahl_id#14.stadtbez omitted because of collinearity.
note: 8.wahl_id#15.stadtbez omitted because of collinearity.
note: 8.wahl_id#16.stadtbez omitted because of collinearity.
note: 8.wahl_id#17.stadtbez omitted because of collinearity.
note: 8.wahl_id#18.stadtbez omitted because of collinearity.
note: 8.wahl_id#19.stadtbez omitted because of collinearity.
note: 8.wahl_id#20.stadtbez omitted because of collinearity.
note: 8.wahl_id#21.stadtbez omitted because of collinearity.
note: 8.wahl_id#22.stadtbez omitted because of collinearity.
note: 8.wahl_id#23.stadtbez omitted because of collinearity.
note: 8.wahl_id#24.stadtbez omitted because of collinearity.
note: 8.wahl_id#25.stadtbez omitted because of collinearity.

Linear regression, absorbing indicators            Number of obs     =   4,666
Absorbed variable: sb_new                          No. of categories =     618
                                                   F(203, 617)       = 1871.16
                                                   Prob > F          =  0.0000
                                                   R-squared         =  0.9903
                                                   Adj R-squared     =  0.9883
                                                   Root MSE          =  1.6194

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3501733    -0.24   0.814    -.7700858    .6052663
          F6event |   .2549675   .2967038     0.86   0.390    -.3277043    .8376393
          F5event |  -.2369689   .2814088    -0.84   0.400     -.789604    .3156662
          F4event |  -.2192898   .1820885    -1.20   0.229    -.5768781    .1382985
          F3event |  -.0490231   .1697212    -0.29   0.773    -.3823243    .2842782
          F2event |  -.0537065   .1420691    -0.38   0.706    -.3327042    .2252911
          L0event |  -.3892706   .1755396    -2.22   0.027    -.7339982   -.0445431
          L1event |    .011072   .2158926     0.05   0.959    -.4129015    .4350455
          L2event |   .2959665    .240925     1.23   0.220    -.1771659     .769099
          L3event |   .1243464   .2505613     0.50   0.620    -.3677101    .6164028
          L4event |   .6528057   .6668744     0.98   0.328    -.6568132    1.962425
          L5event |   1.807978    .766304     2.36   0.019     .3030973    3.312858
          L6event |   .6923315   .8543273     0.81   0.418    -.9854104    2.370073
          L7event |   .4397263   1.181706     0.37   0.710    -1.880927    2.760379
                  |
 wahl_id#stadtbez |
        LTW13# 2  |   2.599337   .7886391     3.30   0.001     1.050595    4.148079
        LTW13# 3  |   2.848403   .7554065     3.77   0.000     1.364923    4.331882
        LTW13# 4  |   3.485134    .717273     4.86   0.000     2.076542    4.893727
        LTW13# 5  |   2.337836   .9169731     2.55   0.011     .5370695    4.138603
        LTW13# 6  |   2.390768   .7810109     3.06   0.002     .8570062     3.92453
        LTW13# 7  |   2.890573   .7242833     3.99   0.000     1.468214    4.312933
        LTW13# 8  |   2.607575   1.270925     2.05   0.041     .1117124    5.103437
        LTW13# 9  |     4.3143   .6192678     6.97   0.000     3.098172    5.530428
        LTW13#10  |    3.83766   .7377796     5.20   0.000     2.388796    5.286523
        LTW13#11  |    3.21033   .8644321     3.71   0.000     1.512744    4.907916
        LTW13#12  |   3.207361   .6780912     4.73   0.000     1.875715    4.539008
        LTW13#13  |   5.223425   .5664611     9.22   0.000     4.110999     6.33585
        LTW13#14  |   3.121159   .6913224     4.51   0.000     1.763529    4.478789
        LTW13#15  |   4.652573   .7345735     6.33   0.000     3.210006     6.09514
        LTW13#16  |   4.199513   .6284597     6.68   0.000     2.965334    5.433693
        LTW13#17  |   2.486644   .7258832     3.43   0.001     1.061143    3.912146
        LTW13#18  |   2.350396   .8296256     2.83   0.005     .7211635    3.979628
        LTW13#19  |   4.326565   .6281389     6.89   0.000     3.093015    5.560114
        LTW13#20  |   4.724136   .6878232     6.87   0.000     3.373378    6.074895
        LTW13#21  |   4.753861   .5793484     8.21   0.000     3.616127    5.891595
        LTW13#22  |   5.684304   .7454164     7.63   0.000     4.220443    7.148165
        LTW13#23  |   4.414482   .7453624     5.92   0.000     2.950727    5.878236
        LTW13#24  |   2.720643   .6237115     4.36   0.000     1.495788    3.945497
        LTW13#25  |   4.014852   .7594413     5.29   0.000     2.523449    5.506255
        BTW13# 1  |   8.424427   .3362532    25.05   0.000     7.764088    9.084766
        BTW13# 2  |   10.79048   .7629355    14.14   0.000     9.292214    12.28874
        BTW13# 3  |   12.36154   .7117129    17.37   0.000     10.96387    13.75921
        BTW13# 4  |   11.73683   .7093605    16.55   0.000     10.34378    13.12988
        BTW13# 5  |   10.03349   .9212912    10.89   0.000     8.224245    11.84274
        BTW13# 6  |   9.813611   .7951391    12.34   0.000     8.252103    11.37512
        BTW13# 7  |   9.966512   .7577344    13.15   0.000     8.478461    11.45456
        BTW13# 8  |   10.17036   1.162428     8.75   0.000     7.887567    12.45316
        BTW13# 9  |    11.4886   .6124829    18.76   0.000      10.2858     12.6914
        BTW13#10  |   10.74498   .7559586    14.21   0.000     9.260411    12.22954
        BTW13#11  |   11.32702    .882311    12.84   0.000     9.594322    13.05972
        BTW13#12  |   11.48417   .7101963    16.17   0.000     10.08947    12.87886
        BTW13#13  |   12.86963   .6141288    20.96   0.000      11.6636    14.07567
        BTW13#14  |   10.52395   .7859648    13.39   0.000      8.98046    12.06744
        BTW13#15  |   11.40398   .6937963    16.44   0.000     10.04149    12.76647
        BTW13#16  |   11.17598   .6309008    17.71   0.000     9.937009    12.41496
        BTW13#17  |   10.54011   .6831346    15.43   0.000     9.198558    11.88166
        BTW13#18  |   9.305854   .8666411    10.74   0.000      7.60393    11.00778
        BTW13#19  |   11.68743   .5719599    20.43   0.000      10.5642    12.81065
        BTW13#20  |   11.39114   .6534454    17.43   0.000     10.10789    12.67438
        BTW13#21  |   11.01513   .6203546    17.76   0.000     9.796868    12.23339
        BTW13#22  |   11.86032   .8429659    14.07   0.000     10.20489    13.51575
        BTW13#23  |    10.4919   .8144677    12.88   0.000     8.892435    12.09136
        BTW13#24  |   9.198437   .6889305    13.35   0.000     7.845504    10.55137
        BTW13#25  |   11.19726   .7552676    14.83   0.000      9.71405    12.68046
        KOW14# 1  |  -13.87712   .5828444   -23.81   0.000    -15.02172   -12.73252
        KOW14# 2  |  -12.17121   .6267998   -19.42   0.000    -13.40213   -10.94029
        KOW14# 3  |   -13.0331   .5853254   -22.27   0.000    -14.18258   -11.88363
        KOW14# 4  |  -10.78521   .6108495   -17.66   0.000     -11.9848    -9.58561
        KOW14# 5  |  -12.15436   .7119897   -17.07   0.000    -13.55257   -10.75614
        KOW14# 6  |  -11.86939   .6479155   -18.32   0.000    -13.14177     -10.597
        KOW14# 7  |  -10.02667    .569966   -17.59   0.000    -11.14598   -8.907363
        KOW14# 8  |  -11.06718   .8002551   -13.83   0.000    -12.63874   -9.495628
        KOW14# 9  |  -10.06261   .5710213   -17.62   0.000    -11.18399   -8.941226
        KOW14#10  |  -8.461406   .6582917   -12.85   0.000     -9.75417   -7.168642
        KOW14#11  |  -7.731675   .6618317   -11.68   0.000    -9.031391   -6.431959
        KOW14#12  |  -11.47398   .6140463   -18.69   0.000    -12.67985    -10.2681
        KOW14#13  |  -9.499776   .5061681   -18.77   0.000     -10.4938   -8.505755
        KOW14#14  |  -11.00856   .6658467   -16.53   0.000    -12.31616   -9.700957
        KOW14#15  |  -11.29845   .5735372   -19.70   0.000    -12.42478   -10.17213
        KOW14#16  |  -8.384121   .5284147   -15.87   0.000    -9.421831   -7.346412
        KOW14#17  |  -10.42422   .6488858   -16.06   0.000    -11.69851   -9.149924
        KOW14#18  |  -11.14865   .5383361   -20.71   0.000    -12.20585   -10.09146
        KOW14#19  |  -9.841695   .5740603   -17.14   0.000    -10.96904   -8.714346
        KOW14#20  |   -9.53974   .7047362   -13.54   0.000    -10.92371   -8.155768
        KOW14#21  |  -8.919723   .5875186   -15.18   0.000     -10.0735   -7.765945
        KOW14#22  |  -8.369497   .6633205   -12.62   0.000    -9.672136   -7.066857
        KOW14#23  |  -7.086942   .8590884    -8.25   0.000    -8.774034   -5.399851
        KOW14#24  |  -9.394625   .7879164   -11.92   0.000    -10.94195   -7.847302
        KOW14#25  |  -9.342949   .6531111   -14.31   0.000    -10.62554   -8.060359
        EUW14# 1  |  -15.13356   .6977535   -21.69   0.000    -16.50382    -13.7633
        EUW14# 2  |  -13.34966   .6472136   -20.63   0.000    -14.62067   -12.07865
        EUW14# 3  |  -13.18494   .7011238   -18.81   0.000    -14.56182   -11.80806
        EUW14# 4  |  -12.80829    .652308   -19.64   0.000     -14.0893   -11.52728
        EUW14# 5  |  -13.98509   .8106794   -17.25   0.000    -15.57711   -12.39306
        EUW14# 6  |  -15.11335   .7472573   -20.23   0.000    -16.58082   -13.64587
        EUW14# 7  |  -13.70391   .6859021   -19.98   0.000     -15.0509   -12.35693
        EUW14# 8  |   -13.7762   .9925547   -13.88   0.000     -15.7254   -11.82701
        EUW14# 9  |  -12.85377   .6111487   -21.03   0.000    -14.05395   -11.65359
        EUW14#10  |  -13.57145   .8668818   -15.66   0.000    -15.27385   -11.86906
        EUW14#11  |   -11.6765   .8868187   -13.17   0.000    -13.41805   -9.934951
        EUW14#12  |  -12.67142   .6226315   -20.35   0.000    -13.89415   -11.44869
        EUW14#13  |  -12.04908   .7291837   -16.52   0.000    -13.48107    -10.6171
        EUW14#14  |  -13.21907   .8831096   -14.97   0.000    -14.95333    -11.4848
        EUW14#15  |  -14.91469   .6315673   -23.62   0.000    -16.15497   -13.67441
        EUW14#16  |  -12.07708   .6394234   -18.89   0.000    -13.33279   -10.82137
        EUW14#17  |  -14.08068   .8734846   -16.12   0.000    -15.79604   -12.36531
        EUW14#18  |   -13.3978   .7481643   -17.91   0.000    -14.86706   -11.92855
        EUW14#19  |  -12.80056   .6073801   -21.08   0.000    -13.99334   -11.60778
        EUW14#20  |  -12.45779   .8628719   -14.44   0.000    -14.15231   -10.76326
        EUW14#21  |  -12.92431   .6705061   -19.28   0.000    -14.24106   -11.60756
        EUW14#22  |  -14.53437    .801353   -18.14   0.000    -16.10808   -12.96066
        EUW14#23  |  -15.40999   .9716914   -15.86   0.000    -17.31821   -13.50176
        EUW14#24  |  -15.84792   .9194518   -17.24   0.000    -17.65356   -14.04229
        EUW14#25  |   -13.0004   .7260974   -17.90   0.000    -14.42632   -11.57447
        BTW17# 1  |   15.81522   .5927995    26.68   0.000     14.65107    16.97937
        BTW17# 2  |   20.21099   .6731484    30.02   0.000     18.88905    21.53293
        BTW17# 3  |   21.06445   .6970908    30.22   0.000     19.69549    22.43341
        BTW17# 4  |   19.57002   .7724667    25.33   0.000     18.05303      21.087
        BTW17# 5  |   18.43048   .9231638    19.96   0.000     16.61756    20.24341
        BTW17# 6  |    18.0208   .7462074    24.15   0.000     16.55538    19.48621
        BTW17# 7  |   18.97694   .5891545    32.21   0.000     17.81995    20.13393
        BTW17# 8  |   19.35284   1.044957    18.52   0.000     17.30074    21.40495
        BTW17# 9  |   19.74586   .5287275    37.35   0.000     18.70754    20.78418
        BTW17#10  |   19.80351   .8108608    24.42   0.000     18.21113    21.39589
        BTW17#11  |   20.38376   .7853229    25.96   0.000     18.84153    21.92599
        BTW17#12  |   20.03762     .63526    31.54   0.000     18.79009    21.28515
        BTW17#13  |   20.37319   .5924948    34.39   0.000     19.20964    21.53675
        BTW17#14  |   19.22934    .747087    25.74   0.000      17.7622    20.69648
        BTW17#15  |   20.29456   .5954166    34.08   0.000     19.12528    21.46385
        BTW17#16  |   19.40488   .6306705    30.77   0.000     18.16636    20.64341
        BTW17#17  |   18.82496   .7503666    25.09   0.000     17.35138    20.29854
        BTW17#18  |    17.8424   .7948752    22.45   0.000     16.28141    19.40339
        BTW17#19  |   19.14364   .5916384    32.36   0.000     17.98177    20.30551
        BTW17#20  |   18.89077   .5916825    31.93   0.000     17.72882    20.05273
        BTW17#21  |   18.58471   .5658528    32.84   0.000     17.47348    19.69594
        BTW17#22  |   20.15944   .7908744    25.49   0.000     18.60631    21.71258
        BTW17#23  |   18.85357   .5720809    32.96   0.000     17.73011    19.97703
        BTW17#24  |   18.71153   .6293429    29.73   0.000     17.47562    19.94744
        BTW17#25  |   19.81724   .7308661    27.11   0.000     18.38196    21.25253
        LTW18# 1  |   10.37443   .8338107    12.44   0.000     8.736982    12.01188
        LTW18# 2  |   14.32492   .6226026    23.01   0.000     13.10224     15.5476
        LTW18# 3  |   14.90837   .6763832    22.04   0.000     13.58008    16.23666
        LTW18# 4  |   14.03494   .7511148    18.69   0.000     12.55988    15.50999
        LTW18# 5  |   12.57604    1.07554    11.69   0.000     10.46388    14.68821
        LTW18# 6  |   13.00695   .7055267    18.44   0.000     11.62142    14.39247
        LTW18# 7  |   13.59778   .6004616    22.65   0.000     12.41858    14.77697
        LTW18# 8  |   13.63504   1.137147    11.99   0.000     11.40189    15.86819
        LTW18# 9  |   14.05595   .5199501    27.03   0.000     13.03486    15.07704
        LTW18#10  |   13.99677   .8019406    17.45   0.000     12.42191    15.57163
        LTW18#11  |   13.65977   .8687949    15.72   0.000     11.95362    15.36593
        LTW18#12  |   13.82708   .6596013    20.96   0.000     12.53175    15.12242
        LTW18#13  |   15.56238   .5733413    27.14   0.000     14.43645    16.68832
        LTW18#14  |    12.6047    .762966    16.52   0.000     11.10638    14.10303
        LTW18#15  |   15.13925   .6050322    25.02   0.000     13.95108    16.32743
        LTW18#16  |   13.37499   .7101943    18.83   0.000      11.9803    14.76968
        LTW18#17  |   13.85061   .6388271    21.68   0.000     12.59607    15.10515
        LTW18#18  |   13.86317    .736115    18.83   0.000     12.41757    15.30876
        LTW18#19  |   14.37471   .6133083    23.44   0.000     13.17029    15.57914
        LTW18#20  |   13.55214   .6454922    21.00   0.000     12.28451    14.81977
        LTW18#21  |   14.25746   .6126105    23.27   0.000     13.05441    15.46052
        LTW18#22  |   15.15796   .8182126    18.53   0.000     13.55114    16.76478
        LTW18#23  |   14.72963   .7715561    19.09   0.000     13.21443    16.24482
        LTW18#24  |   12.59034   .7572394    16.63   0.000     11.10326    14.07742
        LTW18#25  |    13.4559   .7867149    17.10   0.000     11.91094    15.00087
        EUW19# 1  |   5.556166    1.01579     5.47   0.000     3.561341    7.550992
        EUW19# 2  |   10.73053   .5145998    20.85   0.000      9.71995    11.74111
        EUW19# 3  |   10.87726    .560971    19.39   0.000     9.775615     11.9789
        EUW19# 4  |   9.563007   .7948394    12.03   0.000     8.002088    11.12393
        EUW19# 5  |    8.52212   .9491267     8.98   0.000      6.65821    10.38603
        EUW19# 6  |     8.2087   .6607442    12.42   0.000      6.91112     9.50628
        EUW19# 7  |   7.813184   .6028872    12.96   0.000     6.629225    8.997144
        EUW19# 8  |   8.888719   1.086231     8.18   0.000     6.755561    11.02188
        EUW19# 9  |   8.272591   .4965628    16.66   0.000     7.297433    9.247749
        EUW19#10  |   6.512991    .847438     7.69   0.000     4.848778    8.177204
        EUW19#11  |   8.185089    .985811     8.30   0.000     6.249138    10.12104
        EUW19#12  |   8.945632   .6878305    13.01   0.000      7.59486    10.29641
        EUW19#13  |   10.01232   .5883861    17.02   0.000     8.856833     11.1678
        EUW19#14  |   7.140245   .8407091     8.49   0.000     5.489247    8.791244
        EUW19#15  |   8.376169   .4679136    17.90   0.000     7.457273    9.295065
        EUW19#16  |   7.101533   .5444917    13.04   0.000     6.032251    8.170814
        EUW19#17  |   7.358702   .6185431    11.90   0.000     6.143997    8.573407
        EUW19#18  |   8.149835   .7384587    11.04   0.000     6.699638    9.600032
        EUW19#19  |    8.00092   .5084324    15.74   0.000     7.002452    8.999388
        EUW19#20  |   7.112062    .773515     9.19   0.000     5.593021    8.631104
        EUW19#21  |   8.819943    .444268    19.85   0.000     7.947483    9.692404
        EUW19#22  |   6.631329   .7589876     8.74   0.000     5.140816    8.121841
        EUW19#23  |   7.160107   .6528711    10.97   0.000     5.877988    8.442226
        EUW19#24  |   4.505546   .7625436     5.91   0.000     3.008051    6.003042
        EUW19#25  |    8.60065   .6481889    13.27   0.000     7.327726    9.873574
        KOW20# 1  |  -3.637058    .863945    -4.21   0.000    -5.333687   -1.940429
        KOW20# 2  |          0  (omitted)
        KOW20# 3  |          0  (omitted)
        KOW20# 4  |          0  (omitted)
        KOW20# 5  |          0  (omitted)
        KOW20# 6  |          0  (omitted)
        KOW20# 7  |          0  (omitted)
        KOW20# 8  |          0  (omitted)
        KOW20# 9  |          0  (omitted)
        KOW20#10  |          0  (omitted)
        KOW20#11  |          0  (omitted)
        KOW20#12  |          0  (omitted)
        KOW20#13  |          0  (omitted)
        KOW20#14  |          0  (omitted)
        KOW20#15  |          0  (omitted)
        KOW20#16  |          0  (omitted)
        KOW20#17  |          0  (omitted)
        KOW20#18  |          0  (omitted)
        KOW20#19  |          0  (omitted)
        KOW20#20  |          0  (omitted)
        KOW20#21  |          0  (omitted)
        KOW20#22  |          0  (omitted)
        KOW20#23  |          0  (omitted)
        KOW20#24  |          0  (omitted)
        KOW20#25  |          0  (omitted)
                  |
        ln_ew_ges |   1.678109   1.148095     1.46   0.144    -.5765383    3.932755
         ew_biodt |    .761748   .0337616    22.56   0.000     .6954465    .8280496
        ew_dtmihi |  -.1673742   .0561113    -2.98   0.003    -.2775664   -.0571819
         ew_ledig |   .4238555   .0751062     5.64   0.000     .2763607    .5713504
       ew_married |   .6387186   .0739777     8.63   0.000     .4934399    .7839972
        wb_anteil |  -.5320422   .0257325   -20.68   0.000    -.5825761   -.4815083
          wb_ausl |  -.0535363   .0189001    -2.83   0.005    -.0906526   -.0164199
         wb_18t24 |  -.0438039   .0278289    -1.57   0.116    -.0984547    .0108469
         wb_25t34 |  -.0197863   .0179066    -1.10   0.270    -.0549516     .015379
         wb_35t44 |  -.0028286   .0224529    -0.13   0.900    -.0469221    .0412649
         wb_45t59 |  -.0241236   .0211331    -1.14   0.254    -.0656252     .017378
          avg_dur |   .0170995   .0236937     0.72   0.471    -.0294307    .0636297
          hh_kids |  -.1148866   .0384133    -2.99   0.003    -.1903233   -.0394499
mpreis_flats_rent |   .0148646   .0253666     0.59   0.558    -.0349507      .06468
            _cons |  -3.948436   10.83534    -0.36   0.716    -25.22705    17.33017
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 229

  ---------------------------------------------------------------------------------------------
                                             Effect on total turnout  Effect on total turnout 
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                               -0.22                    -0.22          
                                                     (0.17)                   (0.17)          
   Reassignment (#t-3#)                               -0.05                    -0.05          
                                                     (0.16)                   (0.16)          
   Reassignment (#t-2#)                               -0.05                    -0.05          
                                                     (0.13)                   (0.14)          
   Reassignment (#t+0#)                              -0.39*                   -0.39*          
                                                     (0.16)                   (0.19)          
   Reassignment (#t+1#)                               0.01                     0.01           
                                                     (0.20)                   (0.21)          
   Reassignment (#t+2#)                               0.30                     0.30           
                                                     (0.22)                   (0.21)          
   R2                                                 0.99                     0.99           
   N                                                  4,666                    4,666          
   Precinct FE                                         X                        X             
   Election-District FE                                X                        X             
   Election-District FE                                                                       
   Cluster Precinct                                    X                                      
   Number of Clusters                                  618                    200+618         
   TW Cluster District-Election + Precinct                                      X             
   WRC Precinct                                                                               
  ---------------------------------------------------------------------------------------------


               --------------------------------------------------------------------
                                                          Effect on total turnout 
               --------------------------------------------------------------------
                Reassignment (#t-4#)                               -0.22          
                                                                  [0.191]         
                Reassignment (#t-3#)                               -0.05          
                                                                  [0.743]         
                Reassignment (#t-2#)                               -0.05          
                                                                  [0.667]         
                Reassignment (#t+0#)                               -0.39          
                                                                  [0.025]         
                Reassignment (#t+1#)                               0.01           
                                                                  [0.950]         
                Reassignment (#t+2#)                               0.30           
                                                                  [0.187]         
                R2                                                 0.99           
                N                                                  4,666          
                Precinct FE                                         X             
                Election-District FE                                X             
                Election-District FE                                              
                Cluster Precinct                                                  
                Number of Clusters                                  618           
                TW Cluster District-Election + Precinct                           
                WRC Precinct                                        X             
               --------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)
note: 1b.wahl_id#3.stadtbez omitted because of collinearity
note: 1b.wahl_id#6.stadtbez omitted because of collinearity
note: 1b.wahl_id#14.stadtbez omitted because of collinearity
note: 1b.wahl_id#17.stadtbez omitted because of collinearity
note: 4.wahl_id#11.stadtbez omitted because of collinearity
note: 6.wahl_id#7.stadtbez omitted because of collinearity
note: 6.wahl_id#13.stadtbez omitted because of collinearity
note: 6.wahl_id#16.stadtbez omitted because of collinearity
note: 6.wahl_id#19.stadtbez omitted because of collinearity
note: 6.wahl_id#20.stadtbez omitted because of collinearity
note: 6.wahl_id#22.stadtbez omitted because of collinearity
note: 6.wahl_id#24.stadtbez omitted because of collinearity
note: 7.wahl_id#2.stadtbez omitted because of collinearity
note: 7.wahl_id#8.stadtbez omitted because of collinearity
note: 7.wahl_id#10.stadtbez omitted because of collinearity
note: 7.wahl_id#12.stadtbez omitted because of collinearity
note: 7.wahl_id#18.stadtbez omitted because of collinearity
note: 7.wahl_id#21.stadtbez omitted because of collinearity
note: 8.wahl_id#4.stadtbez omitted because of collinearity
note: 8.wahl_id#5.stadtbez omitted because of collinearity
note: 8.wahl_id#9.stadtbez omitted because of collinearity
note: 8.wahl_id#15.stadtbez omitted because of collinearity
note: 8.wahl_id#23.stadtbez omitted because of collinearity
note: 8.wahl_id#25.stadtbez omitted because of collinearity

Linear regression, absorbing indicators             Number of obs     =  4,666
Absorbed variable: sb_new                           No. of categories =    618
                                                    F(24, 24)         =      .
                                                    Prob > F          =      .
                                                    R-squared         = 0.9903
                                                    Adj R-squared     = 0.9883
                                                    Root MSE          = 1.5830

                                   (Std. err. adjusted for 25 clusters in stadtbez)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3487871    -0.24   0.815    -.8022708    .6374514
          F6event |   .2549675   .2723145     0.94   0.358     -.307062     .816997
          F5event |  -.2369689   .2830957    -0.84   0.411    -.8212496    .3473119
          F4event |  -.2192898   .1934507    -1.13   0.268    -.6185524    .1799729
          F3event |  -.0490231   .1746902    -0.28   0.781    -.4095659    .3115197
          F2event |  -.0537065   .1658899    -0.32   0.749    -.3960865    .2886734
          L0event |  -.3892706   .1773401    -2.20   0.038    -.7552827   -.0232586
          L1event |    .011072   .2139626     0.05   0.959    -.4305252    .4526692
          L2event |   .2959665    .192916     1.53   0.138    -.1021925    .6941256
          L3event |   .1243464   .2830478     0.44   0.664    -.4598357    .7085284
          L4event |   .6528057   .8170353     0.80   0.432    -1.033472    2.339084
          L5event |   1.807978    .594927     3.04   0.006     .5801085    3.035847
          L6event |   .6923315   .8052046     0.86   0.398    -.9695291    2.354192
          L7event |   .4397263   1.056196     0.42   0.681    -1.740156    2.619608
                  |
 wahl_id#stadtbez |
        LTW13# 2  |  -8.131192   .7308708   -11.13   0.000    -9.639635   -6.622749
        LTW13# 3  |          0  (omitted)
        LTW13# 4  |   3.485134   .7007341     4.97   0.000      2.03889    4.931379
        LTW13# 5  |   2.337836    .764275     3.06   0.005     .7604503    3.915222
        LTW13# 6  |          0  (omitted)
        LTW13# 7  |  -10.70721   .4007844   -26.72   0.000    -11.53438   -9.880027
        LTW13# 8  |  -6.281144   .7628412    -8.23   0.000    -7.855571   -4.706717
        LTW13# 9  |     4.3143   .7206461     5.99   0.000     2.826959     5.80164
        LTW13#10  |  -2.675331   .5923127    -4.52   0.000    -3.897805   -1.452858
        LTW13#11  |   14.88683   .6228385    23.90   0.000     13.60135    16.17231
        LTW13#12  |  -5.738271   .5374669   -10.68   0.000    -6.847548   -4.628994
        LTW13#13  |  -10.33896   .3820537   -27.06   0.000    -11.12748   -9.550438
        LTW13#14  |          0  (omitted)
        LTW13#15  |   4.652573   .6492642     7.17   0.000     3.312558    5.992589
        LTW13#16  |  -9.175478   .3681544   -24.92   0.000    -9.935311   -8.415644
        LTW13#17  |          0  (omitted)
        LTW13#18  |   -5.79944   .5194558   -11.16   0.000    -6.871544   -4.727336
        LTW13#19  |  -10.04815   .3462923   -29.02   0.000    -10.76286   -9.333434
        LTW13#20  |  -8.828003   .3935612   -22.43   0.000    -9.640273   -8.015733
        LTW13#21  |  -4.066082   .4552374    -8.93   0.000    -5.005646   -3.126518
        LTW13#22  |  -9.473653   .4272468   -22.17   0.000    -10.35545   -8.591859
        LTW13#23  |   4.414482   .6521184     6.77   0.000     3.068575    5.760388
        LTW13#24  |  -9.869697   .3770229   -26.18   0.000    -10.64783    -9.09156
        LTW13#25  |   4.014852   .7062849     5.68   0.000     2.557151    5.472552
        BTW13# 1  |   8.424427   .0640096   131.61   0.000     8.292318    8.556536
        BTW13# 2  |   .0599504   .7299188     0.08   0.935    -1.446528    1.566429
        BTW13# 3  |   9.513136   .0779381   122.06   0.000      9.35228    9.673993
        BTW13# 4  |   11.73683   .6752691    17.38   0.000     10.34314    13.13052
        BTW13# 5  |   10.03349   .7506757    13.37   0.000     8.484173    11.58281
        BTW13# 6  |   7.422842    .050028   148.37   0.000      7.31959    7.526095
        BTW13# 7  |  -3.631266   .3968658    -9.15   0.000    -4.450357   -2.812175
        BTW13# 8  |   1.281643   .7593097     1.69   0.104    -.2854953    2.848781
        BTW13# 9  |    11.4886   .7025477    16.35   0.000     10.03861    12.93859
        BTW13#10  |   4.231984    .594461     7.12   0.000     3.005077    5.458891
        BTW13#11  |   23.00352    .626379    36.72   0.000     21.71074     24.2963
        BTW13#12  |   2.538533   .5372549     4.73   0.000     1.429693    3.647372
        BTW13#13  |  -2.692751   .3829696    -7.03   0.000    -3.483161    -1.90234
        BTW13#14  |   7.402791   .0260854   283.79   0.000     7.348953    7.456629
        BTW13#15  |   11.40398   .6400518    17.82   0.000     10.08298    12.72498
        BTW13#16  |  -2.199009   .3691342    -5.96   0.000    -2.960864   -1.437153
        BTW13#17  |   8.053464    .034704   232.06   0.000     7.981839     8.12509
        BTW13#18  |   1.156019   .5196383     2.22   0.036     .0835381      2.2285
        BTW13#19  |  -2.687283   .3495523    -7.69   0.000    -3.408723   -1.965842
        BTW13#20  |  -2.161003   .4015295    -5.38   0.000     -2.98972   -1.332287
        BTW13#21  |   2.195187   .4545302     4.83   0.000     1.257083    3.133291
        BTW13#22  |  -3.297638   .4323236    -7.63   0.000    -4.189911   -2.405366
        BTW13#23  |    10.4919   .6398132    16.40   0.000      9.17139    11.81241
        BTW13#24  |  -3.391903   .3820866    -8.88   0.000    -4.180491   -2.603315
        BTW13#25  |   11.19726   .6989259    16.02   0.000     9.754744    12.63977
        KOW14# 1  |  -13.87712   .6526095   -21.26   0.000    -15.22404    -12.5302
        KOW14# 2  |  -22.90174   .6159095   -37.18   0.000    -24.17291   -21.63056
        KOW14# 3  |  -15.88151   .6691342   -23.73   0.000    -17.26253   -14.50048
        KOW14# 4  |  -10.78521   .4514047   -23.89   0.000    -11.71686   -9.853553
        KOW14# 5  |  -12.15436   .4494092   -27.05   0.000    -13.08189   -11.22682
        KOW14# 6  |  -14.26015   .5895747   -24.19   0.000    -15.47698   -13.04333
        KOW14# 7  |  -23.62445   .8300708   -28.46   0.000    -25.33763   -21.91127
        KOW14# 8  |   -19.9559   .6786135   -29.41   0.000    -21.35649   -18.55531
        KOW14# 9  |  -10.06261    .473155   -21.27   0.000    -11.03915   -9.086063
        KOW14#10  |   -14.9744   .5051857   -29.64   0.000    -16.01705   -13.93175
        KOW14#11  |   3.944825   .5668695     6.96   0.000     2.774864    5.114786
        KOW14#12  |  -20.41961   .4866796   -41.96   0.000    -21.42407   -19.41515
        KOW14#13  |  -25.06216   .7353073   -34.08   0.000    -26.57976   -23.54456
        KOW14#14  |  -14.12972   .5579319   -25.33   0.000    -15.28123    -12.9782
        KOW14#15  |  -11.29845   .4224031   -26.75   0.000    -12.17025   -10.42666
        KOW14#16  |  -21.75911   .8230649   -26.44   0.000    -23.45783   -20.06039
        KOW14#17  |  -12.91086   .6468623   -19.96   0.000    -14.24592    -11.5758
        KOW14#18  |  -19.29849   .4571133   -42.22   0.000    -20.24192   -18.35505
        KOW14#19  |  -24.21641   .7379578   -32.82   0.000    -25.73948   -22.69334
        KOW14#20  |  -23.09188   .7520907   -30.70   0.000    -24.64412   -21.53964
        KOW14#21  |  -17.73967   .4017806   -44.15   0.000     -18.5689   -16.91043
        KOW14#22  |  -23.52745   .7359974   -31.97   0.000    -25.04648   -22.00843
        KOW14#23  |  -7.086942    .419162   -16.91   0.000     -7.95205   -6.221835
        KOW14#24  |  -21.98496   .8523815   -25.79   0.000    -23.74419   -20.22574
        KOW14#25  |  -9.342949   .4152082   -22.50   0.000     -10.1999   -8.486001
        EUW14# 1  |  -15.13356   .4932109   -30.68   0.000    -16.15149   -14.11562
        EUW14# 2  |  -24.08019   .4267346   -56.43   0.000    -24.96093   -23.19945
        EUW14# 3  |  -16.03334   .4863662   -32.97   0.000    -17.03715   -15.02953
        EUW14# 4  |  -12.80829   .5569233   -23.00   0.000    -13.95772   -11.65886
        EUW14# 5  |  -13.98509   .5851149   -23.90   0.000    -15.19271   -12.77747
        EUW14# 6  |  -17.50412   .4560488   -38.38   0.000    -18.44535   -16.56288
        EUW14# 7  |  -27.30169    .667469   -40.90   0.000    -28.67928    -25.9241
        EUW14# 8  |  -22.66492   .3189566   -71.06   0.000    -23.32322   -22.00663
        EUW14# 9  |  -12.85377   .5575735   -23.05   0.000    -14.00455   -11.70299
        EUW14#10  |  -20.08445   .2725908   -73.68   0.000    -20.64705   -19.52185
        EUW14#11  |          0  (omitted)
        EUW14#12  |  -21.61705   .2445058   -88.41   0.000    -22.12169   -21.11242
        EUW14#13  |  -27.61147   .6103974   -45.24   0.000    -28.87126   -26.35167
        EUW14#14  |  -16.34022   .4441923   -36.79   0.000    -17.25699   -15.42346
        EUW14#15  |  -14.91469   .4991625   -29.88   0.000    -15.94491   -13.88447
        EUW14#16  |  -25.45207   .6755005   -37.68   0.000    -26.84624   -24.05791
        EUW14#17  |  -16.56732   .4847165   -34.18   0.000    -17.56773   -15.56692
        EUW14#18  |  -21.54764   .2820294   -76.40   0.000    -22.12972   -20.96556
        EUW14#19  |  -27.17527   .5863299   -46.35   0.000    -28.38539   -25.96514
        EUW14#20  |  -26.00993   .5908365   -44.02   0.000    -27.22935    -24.7905
        EUW14#21  |  -21.74425   .2503897   -86.84   0.000    -22.26103   -21.22748
        EUW14#22  |  -29.69233   .5969228   -49.74   0.000    -30.92431   -28.46034
        EUW14#23  |  -15.40999    .523479   -29.44   0.000    -16.49039   -14.32958
        EUW14#24  |  -28.43826   .6720024   -42.32   0.000    -29.82521   -27.05132
        EUW14#25  |   -13.0004    .564488   -23.03   0.000    -14.16544   -11.83535
        BTW17# 1  |   15.81522   .3451336    45.82   0.000      15.1029    16.52754
        BTW17# 2  |   9.480459   .5839185    16.24   0.000      8.27531    10.68561
        BTW17# 3  |   18.21604   .3510256    51.89   0.000     17.49156    18.94053
        BTW17# 4  |   19.57002   .5600053    34.95   0.000     18.41422    20.72581
        BTW17# 5  |   18.43048   .6422816    28.70   0.000     17.10488    19.75609
        BTW17# 6  |   15.63003   .2915553    53.61   0.000     15.02829    16.23177
        BTW17# 7  |   5.379159   .3105688    17.32   0.000     4.738176    6.020142
        BTW17# 8  |   10.46412   .6907018    15.15   0.000     9.038584    11.88966
        BTW17# 9  |   19.74586    .580578    34.01   0.000     18.54761    20.94412
        BTW17#10  |   13.29052    .505739    26.28   0.000     12.24672    14.33431
        BTW17#11  |   32.06026   .7088333    45.23   0.000      30.5973    33.52322
        BTW17#12  |   11.09199   .4753827    23.33   0.000     10.11085    12.07313
        BTW17#13  |   4.810812   .3465666    13.88   0.000     4.095534     5.52609
        BTW17#14  |   16.10818   .2716295    59.30   0.000     15.54757     16.6688
        BTW17#15  |   20.29456   .5362521    37.85   0.000     19.18779    21.40133
        BTW17#16  |   6.029893   .3423611    17.61   0.000     5.323295    6.736492
        BTW17#17  |   16.33832   .2258042    72.36   0.000     15.87228    16.80435
        BTW17#18  |   9.692562   .4631881    20.93   0.000     8.736589    10.64854
        BTW17#19  |    4.76893   .3377592    14.12   0.000     4.071829     5.46603
        BTW17#20  |   5.338633    .372777    14.32   0.000     4.569259    6.108007
        BTW17#21  |   9.764769   .3816932    25.58   0.000     8.976992    10.55254
        BTW17#22  |   5.001487   .3634027    13.76   0.000     4.251461    5.751513
        BTW17#23  |   18.85357   .6005594    31.39   0.000     17.61408    20.09307
        BTW17#24  |   6.121191   .3602961    16.99   0.000     5.377577    6.864806
        BTW17#25  |   19.81724   .6052804    32.74   0.000     18.56801    21.06648
        LTW18# 1  |   10.37443   .3639622    28.50   0.000     9.623252    11.12561
        LTW18# 2  |   3.594393   .5776578     6.22   0.000     2.402166     4.78662
        LTW18# 3  |   12.05996   .4028558    29.94   0.000     11.22851    12.89142
        LTW18# 4  |   14.03494   .6843537    20.51   0.000      12.6225    15.44737
        LTW18# 5  |   12.57604   .7059943    17.81   0.000     11.11894    14.03314
        LTW18# 6  |   10.61618   .3570675    29.73   0.000     9.879228    11.35313
        LTW18# 7  |          0  (omitted)
        LTW18# 8  |   4.746322   .7156414     6.63   0.000     3.269311    6.223333
        LTW18# 9  |   14.05595   .7015038    20.04   0.000     12.60812    15.50378
        LTW18#10  |   7.483779   .6973464    10.73   0.000     6.044527    8.923031
        LTW18#11  |   25.33627   .8563864    29.59   0.000     23.56878    27.10377
        LTW18#12  |   4.881449   .5332308     9.15   0.000     3.780915    5.981984
        LTW18#13  |          0  (omitted)
        LTW18#14  |   9.483545   .3586342    26.44   0.000      8.74336    10.22373
        LTW18#15  |   15.13925   .7258176    20.86   0.000     13.64124    16.63727
        LTW18#16  |          0  (omitted)
        LTW18#17  |   11.36397   .3390182    33.52   0.000     10.66427    12.06367
        LTW18#18  |   5.713332   .5985942     9.54   0.000     4.477894    6.948769
        LTW18#19  |          0  (omitted)
        LTW18#20  |          0  (omitted)
        LTW18#21  |   5.437521   .5724622     9.50   0.000     4.256017    6.619025
        LTW18#22  |          0  (omitted)
        LTW18#23  |   14.72963   .8354509    17.63   0.000     13.00534    16.45391
        LTW18#24  |          0  (omitted)
        LTW18#25  |    13.4559   .8865585    15.18   0.000     11.62614    15.28567
        EUW19# 1  |   5.556166   .7763894     7.16   0.000     3.953777    7.158555
        EUW19# 2  |          0  (omitted)
        EUW19# 3  |   8.028856   .6192813    12.96   0.000     6.750722    9.306989
        EUW19# 4  |   9.563007   .3461477    27.63   0.000     8.848593    10.27742
        EUW19# 5  |    8.52212   .4405446    19.34   0.000     7.612881     9.43136
        EUW19# 6  |   5.817932   .6205084     9.38   0.000     4.537266    7.098598
        EUW19# 7  |  -5.784594   .6653533    -8.69   0.000    -7.157816   -4.411373
        EUW19# 8  |          0  (omitted)
        EUW19# 9  |   8.272591   .3921248    21.10   0.000     7.463286    9.081897
        EUW19#10  |          0  (omitted)
        EUW19#11  |   19.86159    .318489    62.36   0.000     19.20426    20.51892
        EUW19#12  |          0  (omitted)
        EUW19#13  |  -5.550067   .6272985    -8.85   0.000    -6.844748   -4.255387
        EUW19#14  |   4.019086   .6012689     6.68   0.000     2.778128    5.260044
        EUW19#15  |   8.376169    .339578    24.67   0.000     7.675315    9.077023
        EUW19#16  |  -6.273458   .7406499    -8.47   0.000    -7.802084   -4.744832
        EUW19#17  |   4.872058   .5657244     8.61   0.000      3.70446    6.039656
        EUW19#18  |          0  (omitted)
        EUW19#19  |   -6.37379   .6220818   -10.25   0.000    -7.657704   -5.089877
        EUW19#20  |  -6.440077   .6435299   -10.01   0.000    -7.768257   -5.111897
        EUW19#21  |          0  (omitted)
        EUW19#22  |  -8.526629   .6357075   -13.41   0.000    -9.838665   -7.214593
        EUW19#23  |   7.160107    .419076    17.09   0.000     6.295176    8.025037
        EUW19#24  |  -8.084793   .7125719   -11.35   0.000     -9.55547   -6.614117
        EUW19#25  |    8.60065   .4354833    19.75   0.000     7.701857    9.499444
        KOW20# 1  |  -3.637058   .9658689    -3.77   0.001    -5.630513   -1.643602
        KOW20# 2  |  -10.73053   .4545451   -23.61   0.000    -11.66866   -9.792394
        KOW20# 3  |  -2.848403   .7879585    -3.61   0.001    -4.474669   -1.222136
        KOW20# 4  |          0  (omitted)
        KOW20# 5  |          0  (omitted)
        KOW20# 6  |  -2.390768   .8228234    -2.91   0.008    -4.088992   -.6925441
        KOW20# 7  |  -13.59778   .8654773   -15.71   0.000    -15.38404   -11.81152
        KOW20# 8  |  -8.888719   .5472855   -16.24   0.000    -10.01826   -7.759177
        KOW20# 9  |          0  (omitted)
        KOW20#10  |  -6.512991   .4399748   -14.80   0.000    -7.421054   -5.604928
        KOW20#11  |    11.6765   .7150007    16.33   0.000     10.20081    13.15219
        KOW20#12  |  -8.945632   .3757891   -23.80   0.000    -9.721223   -8.170042
        KOW20#13  |  -15.56238   .7886269   -19.73   0.000    -17.19003   -13.93474
        KOW20#14  |  -3.121159   .7433085    -4.20   0.000    -4.655272   -1.587046
        KOW20#15  |          0  (omitted)
        KOW20#16  |  -13.37499   .9251886   -14.46   0.000    -15.28449    -11.4655
        KOW20#17  |  -2.486644   .7706508    -3.23   0.004    -4.077189   -.8960992
        KOW20#18  |  -8.149835   .4339449   -18.78   0.000    -9.045454   -7.254217
        KOW20#19  |  -14.37471    .821689   -17.49   0.000    -16.07059   -12.67883
        KOW20#20  |  -13.55214   .8095669   -16.74   0.000      -15.223   -11.88128
        KOW20#21  |  -8.819943    .355251   -24.83   0.000    -9.553145   -8.086741
        KOW20#22  |  -15.15796   .7937358   -19.10   0.000    -16.79615   -13.51977
        KOW20#23  |          0  (omitted)
        KOW20#24  |  -12.59034   .8911794   -14.13   0.000    -14.42964   -10.75104
        KOW20#25  |          0  (omitted)
                  |
        ln_ew_ges |   1.678109    1.29696     1.29   0.208    -.9986858    4.354903
         ew_biodt |    .761748   .0440064    17.31   0.000     .6709233    .8525727
        ew_dtmihi |  -.1673742   .0635492    -2.63   0.015    -.2985332   -.0362151
         ew_ledig |   .4238555   .0978133     4.33   0.000     .2219789    .6257322
       ew_married |   .6387186   .0835155     7.65   0.000     .4663511     .811086
        wb_anteil |  -.5320422   .0360037   -14.78   0.000    -.6063501   -.4577343
          wb_ausl |  -.0535363   .0275432    -1.94   0.064    -.1103827    .0033102
         wb_18t24 |  -.0438039   .0289648    -1.51   0.144    -.1035843    .0159765
         wb_25t34 |  -.0197863   .0168315    -1.18   0.251    -.0545249    .0149523
         wb_35t44 |  -.0028286   .0318706    -0.09   0.930    -.0686062     .062949
         wb_45t59 |  -.0241236   .0212907    -1.13   0.268    -.0680655    .0198183
          avg_dur |   .0170995   .0242372     0.71   0.487    -.0329237    .0671227
          hh_kids |  -.1148866   .0505215    -2.27   0.032    -.2191579   -.0106154
mpreis_flats_rent |   .0148646   .0297364     0.50   0.622    -.0465084    .0762376
            _cons |    2.59668   13.06025     0.20   0.844    -24.35835    29.55171
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 229

  ---------------------------------------------------------------------------------------------
                                             Effect on total turnout  Effect on total turnout 
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                               -0.22                    -0.22          
                                                     (0.17)                   (0.17)          
   Reassignment (#t-3#)                               -0.05                    -0.05          
                                                     (0.16)                   (0.16)          
   Reassignment (#t-2#)                               -0.05                    -0.05          
                                                     (0.13)                   (0.14)          
   Reassignment (#t+0#)                              -0.39*                   -0.39*          
                                                     (0.16)                   (0.19)          
   Reassignment (#t+1#)                               0.01                     0.01           
                                                     (0.20)                   (0.21)          
   Reassignment (#t+2#)                               0.30                     0.30           
                                                     (0.22)                   (0.21)          
   R2                                                 0.99                     0.99           
   N                                                  4,666                    4,666          
   Precinct FE                                         X                        X             
   Election-District FE                                X                        X             
   Election-District FE                                                                       
   Cluster Precinct                                    X                                      
   Number of Clusters                                  618                    200+618         
   TW Cluster District-Election + Precinct                                      X             
   WRC Precinct                                                                               
   WRC District                                                                               
  ---------------------------------------------------------------------------------------------


  ---------------------------------------------------------------------------------------------
                                             Effect on total turnout  Effect on total turnout 
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                               -0.22                    -0.22          
                                                     [0.191]                  [0.221]         
   Reassignment (#t-3#)                               -0.05                    -0.05          
                                                     [0.743]                  [0.752]         
   Reassignment (#t-2#)                               -0.05                    -0.05          
                                                     [0.667]                  [0.732]         
   Reassignment (#t+0#)                               -0.39                    -0.39          
                                                     [0.025]                  [0.027]         
   Reassignment (#t+1#)                               0.01                     0.01           
                                                     [0.950]                  [0.958]         
   Reassignment (#t+2#)                               0.30                     0.30           
                                                     [0.187]                  [0.097]         
   R2                                                 0.99                     0.99           
   N                                                  4,666                    4,666          
   Precinct FE                                         X                        X             
   Election-District FE                                X                        X             
   Election-District FE                                                                       
   Cluster Precinct                                                                           
   Number of Clusters                                  618                      25            
   TW Cluster District-Election + Precinct                                                    
   WRC Precinct                                        X                                      
   WRC District                                                                 X             
  ---------------------------------------------------------------------------------------------

(sum of wgt is 7,252,238.2531165)

Linear regression, absorbing indicators             Number of obs     =  4,666
Absorbed variable: sb_new                           No. of categories =    618
                                                    F(24, 24)         =      .
                                                    Prob > F          =      .
                                                    R-squared         = 0.9875
                                                    Adj R-squared     = 0.9855
                                                    Root MSE          = 1.7973

                                   (Std. err. adjusted for 25 clusters in stadtbez)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0049977   .3459455     0.01   0.989    -.7089987    .7189941
          F6event |   .1547925   .2852159     0.54   0.592    -.4338641    .7434492
          F5event |  -.2292509   .3088348    -0.74   0.465    -.8666545    .4081527
          F4event |  -.2241174   .1750485    -1.28   0.213    -.5853998    .1371651
          F3event |  -.1519224   .1795455    -0.85   0.406    -.5224862    .2186413
          F2event |  -.0112714   .2071373    -0.05   0.957    -.4387818    .4162389
          L0event |  -.5300509   .1644972    -3.22   0.004    -.8695564   -.1905454
          L1event |  -.0018816   .2302271    -0.01   0.994     -.477047    .4732839
          L2event |   .2664154   .3079816     0.87   0.396    -.3692274    .9020583
          L3event |  -.0110278   .3417052    -0.03   0.975    -.7162726     .694217
          L4event |   1.429649   1.374374     1.04   0.309    -1.406918    4.266217
          L5event |   1.477225   .5685485     2.60   0.016      .303799    2.650652
          L6event |   .4332568   1.103924     0.39   0.698     -1.84513    2.711644
          L7event |   .5087222   .7588049     0.67   0.509    -1.057374    2.074818
                  |
          wahl_id |
           BTW13  |   7.419596    .188844    39.29   0.000     7.029841     7.80935
           KOW14  |  -14.08115   .5696675   -24.72   0.000    -15.25689   -12.90541
           EUW14  |   -17.3416   .5292324   -32.77   0.000    -18.43388   -16.24932
           BTW17  |   15.44719   .3632402    42.53   0.000     14.69749    16.19688
           LTW18  |   9.815137   .3609739    27.19   0.000     9.070123    10.56015
           EUW19  |   3.653511   .6227344     5.87   0.000      2.36825    4.938771
           KOW20  |  -4.561396    .727859    -6.27   0.000    -6.063623   -3.059169
                  |
        ln_ew_ges |   1.650218   1.361759     1.21   0.237    -1.160316    4.460751
         ew_biodt |   .7902138   .0484953    16.29   0.000     .6901244    .8903033
        ew_dtmihi |  -.1665338   .0650011    -2.56   0.017    -.3006894   -.0323781
         ew_ledig |   .3830132   .1037836     3.69   0.001     .1688143     .597212
       ew_married |   .6156742   .0891937     6.90   0.000     .4315875     .799761
        wb_anteil |    -.54133   .0416519   -13.00   0.000    -.6272954   -.4553647
          wb_ausl |  -.0348043    .023461    -1.48   0.151    -.0832254    .0136169
         wb_18t24 |   -.056542   .0286122    -1.98   0.060    -.1155947    .0025108
         wb_25t34 |  -.0117404   .0151637    -0.77   0.446    -.0430368     .019556
         wb_35t44 |  -.0065333   .0254277    -0.26   0.799    -.0590135    .0459468
         wb_45t59 |   -.012029   .0186047    -0.65   0.524    -.0504272    .0263691
          avg_dur |   .0416426   .0233238     1.79   0.087    -.0064953    .0897804
          hh_kids |  -.0901732   .0520161    -1.73   0.096    -.1975291    .0171827
mpreis_flats_rent |   .1036902   .0362766     2.86   0.009     .0288189    .1785615
            _cons |  -.8460093   13.59546    -0.06   0.951    -28.90566    27.21364
-----------------------------------------------------------------------------------

added matrix:
          e(boot_pval) :  1 x 37

  ---------------------------------------------------------------------------------------------
                                             Effect on total turnout  Effect on total turnout 
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                               -0.22                    -0.22          
                                                     (0.17)                   (0.17)          
   Reassignment (#t-3#)                               -0.05                    -0.05          
                                                     (0.16)                   (0.16)          
   Reassignment (#t-2#)                               -0.05                    -0.05          
                                                     (0.13)                   (0.14)          
   Reassignment (#t+0#)                              -0.39*                   -0.39*          
                                                     (0.16)                   (0.19)          
   Reassignment (#t+1#)                               0.01                     0.01           
                                                     (0.20)                   (0.21)          
   Reassignment (#t+2#)                               0.30                     0.30           
                                                     (0.22)                   (0.21)          
   R2                                                 0.99                     0.99           
   N                                                  4,666                    4,666          
   Precinct FE                                         X                        X             
   Election-District FE                                X                        X             
   Election-District FE                                                                       
   Cluster Precinct                                    X                                      
   Number of Clusters                                  618                    200+618         
   TW Cluster District-Election + Precinct                                      X             
   WRC Precinct                                                                               
   WRC District                                                                               
   Election FE                                                                                
  ---------------------------------------------------------------------------------------------


  ---------------------------------------------------------------------------------------------
                                             Effect on total turnout  Effect on total turnout 
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                               -0.22                    -0.22          
                                                     [0.191]                  [0.221]         
   Reassignment (#t-3#)                               -0.05                    -0.05          
                                                     [0.743]                  [0.752]         
   Reassignment (#t-2#)                               -0.05                    -0.05          
                                                     [0.667]                  [0.732]         
   Reassignment (#t+0#)                               -0.39                    -0.39          
                                                     [0.025]                  [0.027]         
   Reassignment (#t+1#)                               0.01                     0.01           
                                                     [0.950]                  [0.958]         
   Reassignment (#t+2#)                               0.30                     0.30           
                                                     [0.187]                  [0.097]         
   R2                                                 0.99                     0.99           
   N                                                  4,666                    4,666          
   Precinct FE                                         X                        X             
   Election-District FE                                X                        X             
   Election-District FE                                                                       
   Cluster Precinct                                                                           
   Number of Clusters                                  618                      25            
   TW Cluster District-Election + Precinct                                                    
   WRC Precinct                                        X                                      
   WRC District                                                                 X             
   Election FE                                                                                
  ---------------------------------------------------------------------------------------------


               --------------------------------------------------------------------
                                                          Effect on total turnout 
               --------------------------------------------------------------------
                Reassignment (#t-4#)                               -0.22          
                                                                  [0.189]         
                Reassignment (#t-3#)                               -0.15          
                                                                  [0.375]         
                Reassignment (#t-2#)                               -0.01          
                                                                  [0.957]         
                Reassignment (#t+0#)                               -0.53          
                                                                  [0.003]         
                Reassignment (#t+1#)                               -0.00          
                                                                  [0.994]         
                Reassignment (#t+2#)                               0.27           
                                                                  [0.408]         
                R2                                                 0.99           
                N                                                  4,666          
                Precinct FE                                         X             
                Election-District FE                                              
                Election-District FE                                              
                Cluster Precinct                                                  
                Number of Clusters                                  25            
                TW Cluster District-Election + Precinct                           
                WRC Precinct                                                      
                WRC District                                        X             
                Election FE                                         X             
               --------------------------------------------------------------------


  ---------------------------------------------------------------------------------------------
                                             Effect on total turnout  Effect on total turnout 
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                               -0.22                    -0.22          
                                                     (0.17)                   (0.17)          
   Reassignment (#t-3#)                               -0.05                    -0.05          
                                                     (0.16)                   (0.16)          
   Reassignment (#t-2#)                               -0.05                    -0.05          
                                                     (0.13)                   (0.14)          
   Reassignment (#t+0#)                              -0.39*                   -0.39*          
                                                     (0.16)                   (0.19)          
   Reassignment (#t+1#)                               0.01                     0.01           
                                                     (0.20)                   (0.21)          
   Reassignment (#t+2#)                               0.30                     0.30           
                                                     (0.22)                   (0.21)          
   R2                                                 0.99                     0.99           
   N                                                  4,666                    4,666          
   Precinct FE                                         X                        X             
   Election-District FE                                X                        X             
   Election-District FE                                                                       
   Cluster Precinct                                    X                                      
   Number of Clusters                                  618                    200+618         
   TW Cluster District-Election + Precinct                                      X             
   WRC Precinct                                                                               
   WRC District                                                                               
   Election FE                                                                                
  ---------------------------------------------------------------------------------------------


  ---------------------------------------------------------------------------------------------
                                             Effect on total turnout  Effect on total turnout 
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                               -0.22                    -0.22          
                                                     [0.191]                  [0.221]         
   Reassignment (#t-3#)                               -0.05                    -0.05          
                                                     [0.743]                  [0.752]         
   Reassignment (#t-2#)                               -0.05                    -0.05          
                                                     [0.667]                  [0.732]         
   Reassignment (#t+0#)                               -0.39                    -0.39          
                                                     [0.025]                  [0.027]         
   Reassignment (#t+1#)                               0.01                     0.01           
                                                     [0.950]                  [0.958]         
   Reassignment (#t+2#)                               0.30                     0.30           
                                                     [0.187]                  [0.097]         
   R2                                                 0.99                     0.99           
   N                                                  4,666                    4,666          
   Precinct FE                                         X                        X             
   Election-District FE                                X                        X             
   Election-District FE                                                                       
   Cluster Precinct                                                                           
   Number of Clusters                                  618                      25            
   TW Cluster District-Election + Precinct                                                    
   WRC Precinct                                        X                                      
   WRC District                                                                 X             
   Election FE                                                                                
  ---------------------------------------------------------------------------------------------


               --------------------------------------------------------------------
                                                          Effect on total turnout 
               --------------------------------------------------------------------
                Reassignment (#t-4#)                               -0.22          
                                                                  [0.189]         
                Reassignment (#t-3#)                               -0.15          
                                                                  [0.375]         
                Reassignment (#t-2#)                               -0.01          
                                                                  [0.957]         
                Reassignment (#t+0#)                               -0.53          
                                                                  [0.003]         
                Reassignment (#t+1#)                               -0.00          
                                                                  [0.994]         
                Reassignment (#t+2#)                               0.27           
                                                                  [0.408]         
                R2                                                 0.99           
                N                                                  4,666          
                Precinct FE                                         X             
                Election-District FE                                              
                Election-District FE                                              
                Cluster Precinct                                                  
                Number of Clusters                                  25            
                TW Cluster District-Election + Precinct                           
                WRC Precinct                                                      
                WRC District                                        X             
                Election FE                                         X             
               --------------------------------------------------------------------


.                  outreg, replay(turnout_urne) ctitle("", "\makecell{Cluster \\ Precinct \\ (base
> line)}","\makecell{TW Cluster \\ Precinct+ \\ Election-District}", ///
>                                 "\makecell{Wild Cluster \\ Bootstrap \\ Precinct}","\makecell{Wi
> ld Cluster \\ Bootstrap \\ District}","\makecell{Wild Cluster \\ Bootstrap \\ District}"\    ///
>                 "", (1), (2), (3), (4), (5) \ "\midrule" \ "\multicolumn{3}{l}{\textbf{Panel A:}
>  Effect on Turnout at the Polling Place}" \"\midrule") store(tab1) ///
>                                 addrow("\midrule" \ "\multicolumn{3}{l}{\textbf{Panel B:} Effect
>  on Turnout via Mail}" \ "\midrule") 
warning: matrix in ctitles option has varying size rows:
   "", "\makecell{Cluster \\ Precinct \\ (baseline)}","\makecell{TW Cluster \\ Precinct+ \\ Electi
> on-District}",                                 "\makecell{Wild Cluster \\ Bootstrap \\ Precinct}
> ","\makecell{Wild Cluster \\ Bootstrap \\ District}","\makecell{Wild Cluster \\ Bootstrap \\ Dis
> trict}"\                    "", (1), (2), (3), (4), (5) \ "\midrule" \ "\multicolumn{3}{l}{\text
> bf{Panel A:} Effect on Turnout at the Polling Place}" \"\midrule"

                                           {hline 333}
                                                                                \makecell{Cluster 
> \ Precinct \ (baseline)}    \makecell{TW Cluster \ Precinct+ \ Election-District}    \makecell{W
> ild Cluster \ Bootstrap \ Precinct}    \makecell{Wild Cluster \ Bootstrap \ District}    \makece
> ll{Wild Cluster \ Bootstrap \ District}  
                                                                                                  
>  (1)                                                 (2)                                        
>           (3)                                               (4)                                 
>               (5)                        
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
                                           {hline 333}
Reassignment (#t-4#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.12                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.946]                                           [0.946]                               
>             [0.530]                      
Reassignment (#t-3#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.04                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.749]                                           [0.769]                               
>             [0.850]                      
Reassignment (#t-2#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>               0.15                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.958]                                           [0.961]                               
>             [0.348]                      
Reassignment (#t+0#)                                                                             -
> 1.00***                                           -1.00***                                      
>          -1.00                                             -1.00                                
>              -1.07                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.000]                               
>             [0.001]                      
Reassignment (#t+1#)                                                                             -
> 0.89***                                           -0.89***                                      
>          -0.89                                             -0.89                                
>              -0.87                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.002]                               
>             [0.029]                      
Reassignment (#t+2#)                                                                             -
> 0.75**                                             -0.75**                                      
>          -0.75                                             -0.75                                
>              -0.70                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.001]                                           [0.031]                               
>             [0.052]                      
R2                                                                                                
>  0.97                                               0.97                                        
>           0.97                                              0.97                                
>               0.96                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
                                           {hline 333}


.                  outreg, replay(tab1) append(turnout_pos_req) store(tab_urne_postal_req)

                                           {hline 333}
                                                                                \makecell{Cluster 
> \ Precinct \ (baseline)}    \makecell{TW Cluster \ Precinct+ \ Election-District}    \makecell{W
> ild Cluster \ Bootstrap \ Precinct}    \makecell{Wild Cluster \ Bootstrap \ District}    \makece
> ll{Wild Cluster \ Bootstrap \ District}  
                                                                                                  
>  (1)                                                 (2)                                        
>           (3)                                               (4)                                 
>               (5)                        
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
                                           {hline 333}
Reassignment (#t-4#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.12                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.946]                                           [0.946]                               
>             [0.530]                      
Reassignment (#t-3#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.04                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.749]                                           [0.769]                               
>             [0.850]                      
Reassignment (#t-2#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>               0.15                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.958]                                           [0.961]                               
>             [0.348]                      
Reassignment (#t+0#)                                                                             -
> 1.00***                                           -1.00***                                      
>          -1.00                                             -1.00                                
>              -1.07                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.000]                               
>             [0.001]                      
Reassignment (#t+1#)                                                                             -
> 0.89***                                           -0.89***                                      
>          -0.89                                             -0.89                                
>              -0.87                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.002]                               
>             [0.029]                      
Reassignment (#t+2#)                                                                             -
> 0.75**                                             -0.75**                                      
>          -0.75                                             -0.75                                
>              -0.70                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.001]                                           [0.031]                               
>             [0.052]                      
R2                                                                                                
>  0.97                                               0.97                                        
>           0.97                                              0.97                                
>               0.96                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
Reassignment (#t-4#)                                                                              
> -0.23                                               -0.23                                       
>          -0.23                                             -0.23                                
>              -0.11                       
                                                                                                  
> (0.16)                                             (0.16)                                       
>         [0.146]                                           [0.233]                               
>             [0.486]                      
Reassignment (#t-3#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.11                       
                                                                                                  
> (0.15)                                             (0.16)                                       
>         [0.963]                                           [0.963]                               
>             [0.603]                      
Reassignment (#t-2#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.17                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.636]                                           [0.637]                               
>             [0.400]                      
Reassignment (#t+0#)                                                                              
> 0.61**                                             0.61**                                       
>           0.61                                              0.61                                
>               0.54                       
                                                                                                  
> (0.22)                                             (0.23)                                       
>         [0.012]                                           [0.016]                               
>             [0.063]                      
Reassignment (#t+1#)                                                                             0
> .90***                                             0.90***                                      
>           0.90                                              0.90                                
>               0.87                       
                                                                                                  
> (0.23)                                             (0.25)                                       
>         [0.001]                                           [0.002]                               
>             [0.016]                      
Reassignment (#t+2#)                                                                             1
> .05***                                             1.05***                                      
>           1.05                                              1.05                                
>               0.97                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.000]                                           [0.000]                               
>             [0.012]                      
R2                                                                                                
>  0.96                                               0.96                                        
>           0.96                                              0.96                                
>               0.95                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
                                           {hline 333}


.                 
.                  outreg, replay(tab_urne_postal_req) store(tab3) ///
>                                 addrow("\midrule" \ "\multicolumn{3}{l}{\textbf{Panel C:} Effect
>  on Total Turnout}" \ "\midrule")  

                                           {hline 333}
                                                                                \makecell{Cluster 
> \ Precinct \ (baseline)}    \makecell{TW Cluster \ Precinct+ \ Election-District}    \makecell{W
> ild Cluster \ Bootstrap \ Precinct}    \makecell{Wild Cluster \ Bootstrap \ District}    \makece
> ll{Wild Cluster \ Bootstrap \ District}  
                                                                                                  
>  (1)                                                 (2)                                        
>           (3)                                               (4)                                 
>               (5)                        
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
                                           {hline 333}
Reassignment (#t-4#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.12                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.946]                                           [0.946]                               
>             [0.530]                      
Reassignment (#t-3#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.04                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.749]                                           [0.769]                               
>             [0.850]                      
Reassignment (#t-2#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>               0.15                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.958]                                           [0.961]                               
>             [0.348]                      
Reassignment (#t+0#)                                                                             -
> 1.00***                                           -1.00***                                      
>          -1.00                                             -1.00                                
>              -1.07                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.000]                               
>             [0.001]                      
Reassignment (#t+1#)                                                                             -
> 0.89***                                           -0.89***                                      
>          -0.89                                             -0.89                                
>              -0.87                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.002]                               
>             [0.029]                      
Reassignment (#t+2#)                                                                             -
> 0.75**                                             -0.75**                                      
>          -0.75                                             -0.75                                
>              -0.70                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.001]                                           [0.031]                               
>             [0.052]                      
R2                                                                                                
>  0.97                                               0.97                                        
>           0.97                                              0.97                                
>               0.96                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
Reassignment (#t-4#)                                                                              
> -0.23                                               -0.23                                       
>          -0.23                                             -0.23                                
>              -0.11                       
                                                                                                  
> (0.16)                                             (0.16)                                       
>         [0.146]                                           [0.233]                               
>             [0.486]                      
Reassignment (#t-3#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.11                       
                                                                                                  
> (0.15)                                             (0.16)                                       
>         [0.963]                                           [0.963]                               
>             [0.603]                      
Reassignment (#t-2#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.17                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.636]                                           [0.637]                               
>             [0.400]                      
Reassignment (#t+0#)                                                                              
> 0.61**                                             0.61**                                       
>           0.61                                              0.61                                
>               0.54                       
                                                                                                  
> (0.22)                                             (0.23)                                       
>         [0.012]                                           [0.016]                               
>             [0.063]                      
Reassignment (#t+1#)                                                                             0
> .90***                                             0.90***                                      
>           0.90                                              0.90                                
>               0.87                       
                                                                                                  
> (0.23)                                             (0.25)                                       
>         [0.001]                                           [0.002]                               
>             [0.016]                      
Reassignment (#t+2#)                                                                             1
> .05***                                             1.05***                                      
>           1.05                                              1.05                                
>               0.97                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.000]                                           [0.000]                               
>             [0.012]                      
R2                                                                                                
>  0.96                                               0.96                                        
>           0.96                                              0.96                                
>               0.95                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel C:} Effect on Total Turnout}                                     
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
                                           {hline 333}


.                  outreg, replay(tab3) append(turnout_tot_req) store(tab_urne_postal_tot_req)

                                           {hline 333}
                                                                                \makecell{Cluster 
> \ Precinct \ (baseline)}    \makecell{TW Cluster \ Precinct+ \ Election-District}    \makecell{W
> ild Cluster \ Bootstrap \ Precinct}    \makecell{Wild Cluster \ Bootstrap \ District}    \makece
> ll{Wild Cluster \ Bootstrap \ District}  
                                                                                                  
>  (1)                                                 (2)                                        
>           (3)                                               (4)                                 
>               (5)                        
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
                                           {hline 333}
Reassignment (#t-4#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.12                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.946]                                           [0.946]                               
>             [0.530]                      
Reassignment (#t-3#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.04                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.749]                                           [0.769]                               
>             [0.850]                      
Reassignment (#t-2#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>               0.15                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.958]                                           [0.961]                               
>             [0.348]                      
Reassignment (#t+0#)                                                                             -
> 1.00***                                           -1.00***                                      
>          -1.00                                             -1.00                                
>              -1.07                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.000]                               
>             [0.001]                      
Reassignment (#t+1#)                                                                             -
> 0.89***                                           -0.89***                                      
>          -0.89                                             -0.89                                
>              -0.87                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.002]                               
>             [0.029]                      
Reassignment (#t+2#)                                                                             -
> 0.75**                                             -0.75**                                      
>          -0.75                                             -0.75                                
>              -0.70                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.001]                                           [0.031]                               
>             [0.052]                      
R2                                                                                                
>  0.97                                               0.97                                        
>           0.97                                              0.97                                
>               0.96                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
Reassignment (#t-4#)                                                                              
> -0.23                                               -0.23                                       
>          -0.23                                             -0.23                                
>              -0.11                       
                                                                                                  
> (0.16)                                             (0.16)                                       
>         [0.146]                                           [0.233]                               
>             [0.486]                      
Reassignment (#t-3#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.11                       
                                                                                                  
> (0.15)                                             (0.16)                                       
>         [0.963]                                           [0.963]                               
>             [0.603]                      
Reassignment (#t-2#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.17                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.636]                                           [0.637]                               
>             [0.400]                      
Reassignment (#t+0#)                                                                              
> 0.61**                                             0.61**                                       
>           0.61                                              0.61                                
>               0.54                       
                                                                                                  
> (0.22)                                             (0.23)                                       
>         [0.012]                                           [0.016]                               
>             [0.063]                      
Reassignment (#t+1#)                                                                             0
> .90***                                             0.90***                                      
>           0.90                                              0.90                                
>               0.87                       
                                                                                                  
> (0.23)                                             (0.25)                                       
>         [0.001]                                           [0.002]                               
>             [0.016]                      
Reassignment (#t+2#)                                                                             1
> .05***                                             1.05***                                      
>           1.05                                              1.05                                
>               0.97                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.000]                                           [0.000]                               
>             [0.012]                      
R2                                                                                                
>  0.96                                               0.96                                        
>           0.96                                              0.96                                
>               0.95                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel C:} Effect on Total Turnout}                                     
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
Reassignment (#t-4#)                                                                              
> -0.22                                               -0.22                                       
>          -0.22                                             -0.22                                
>              -0.22                       
                                                                                                  
> (0.17)                                             (0.17)                                       
>         [0.191]                                           [0.221]                               
>             [0.189]                      
Reassignment (#t-3#)                                                                              
> -0.05                                               -0.05                                       
>          -0.05                                             -0.05                                
>              -0.15                       
                                                                                                  
> (0.16)                                             (0.16)                                       
>         [0.743]                                           [0.752]                               
>             [0.375]                      
Reassignment (#t-2#)                                                                              
> -0.05                                               -0.05                                       
>          -0.05                                             -0.05                                
>              -0.01                       
                                                                                                  
> (0.13)                                             (0.14)                                       
>         [0.667]                                           [0.732]                               
>             [0.957]                      
Reassignment (#t+0#)                                                                              
> -0.39*                                             -0.39*                                       
>          -0.39                                             -0.39                                
>              -0.53                       
                                                                                                  
> (0.16)                                             (0.19)                                       
>         [0.025]                                           [0.027]                               
>             [0.003]                      
Reassignment (#t+1#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.00                       
                                                                                                  
> (0.20)                                             (0.21)                                       
>         [0.950]                                           [0.958]                               
>             [0.994]                      
Reassignment (#t+2#)                                                                              
>  0.30                                               0.30                                        
>           0.30                                              0.30                                
>               0.27                       
                                                                                                  
> (0.22)                                             (0.21)                                       
>         [0.187]                                           [0.097]                               
>             [0.408]                      
R2                                                                                                
>  0.99                                               0.99                                        
>           0.99                                              0.99                                
>               0.99                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
                                           {hline 333}


.                 
.                 
.                 // Export Table
.                 outreg using "$tables/Table_C2_ES_rob_clustering" , replay(tab_urne_postal_tot_r
> eq) replace tex fragment
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_C2
> _ES_rob_clustering.tex not found)
                                           {hline 333}
                                                                                \makecell{Cluster 
> \ Precinct \ (baseline)}    \makecell{TW Cluster \ Precinct+ \ Election-District}    \makecell{W
> ild Cluster \ Bootstrap \ Precinct}    \makecell{Wild Cluster \ Bootstrap \ District}    \makece
> ll{Wild Cluster \ Bootstrap \ District}  
                                                                                                  
>  (1)                                                 (2)                                        
>           (3)                                               (4)                                 
>               (5)                        
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}                      
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
                                           {hline 333}
Reassignment (#t-4#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.12                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.946]                                           [0.946]                               
>             [0.530]                      
Reassignment (#t-3#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.04                       
                                                                                                  
> (0.17)                                             (0.19)                                       
>         [0.749]                                           [0.769]                               
>             [0.850]                      
Reassignment (#t-2#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>               0.15                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.958]                                           [0.961]                               
>             [0.348]                      
Reassignment (#t+0#)                                                                             -
> 1.00***                                           -1.00***                                      
>          -1.00                                             -1.00                                
>              -1.07                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.000]                               
>             [0.001]                      
Reassignment (#t+1#)                                                                             -
> 0.89***                                           -0.89***                                      
>          -0.89                                             -0.89                                
>              -0.87                       
                                                                                                  
> (0.23)                                             (0.26)                                       
>         [0.000]                                           [0.002]                               
>             [0.029]                      
Reassignment (#t+2#)                                                                             -
> 0.75**                                             -0.75**                                      
>          -0.75                                             -0.75                                
>              -0.70                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.001]                                           [0.031]                               
>             [0.052]                      
R2                                                                                                
>  0.97                                               0.97                                        
>           0.97                                              0.97                                
>               0.96                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
Reassignment (#t-4#)                                                                              
> -0.23                                               -0.23                                       
>          -0.23                                             -0.23                                
>              -0.11                       
                                                                                                  
> (0.16)                                             (0.16)                                       
>         [0.146]                                           [0.233]                               
>             [0.486]                      
Reassignment (#t-3#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.11                       
                                                                                                  
> (0.15)                                             (0.16)                                       
>         [0.963]                                           [0.963]                               
>             [0.603]                      
Reassignment (#t-2#)                                                                              
> -0.06                                               -0.06                                       
>          -0.06                                             -0.06                                
>              -0.17                       
                                                                                                  
> (0.12)                                             (0.14)                                       
>         [0.636]                                           [0.637]                               
>             [0.400]                      
Reassignment (#t+0#)                                                                              
> 0.61**                                             0.61**                                       
>           0.61                                              0.61                                
>               0.54                       
                                                                                                  
> (0.22)                                             (0.23)                                       
>         [0.012]                                           [0.016]                               
>             [0.063]                      
Reassignment (#t+1#)                                                                             0
> .90***                                             0.90***                                      
>           0.90                                              0.90                                
>               0.87                       
                                                                                                  
> (0.23)                                             (0.25)                                       
>         [0.001]                                           [0.002]                               
>             [0.016]                      
Reassignment (#t+2#)                                                                             1
> .05***                                             1.05***                                      
>           1.05                                              1.05                                
>               0.97                       
                                                                                                  
> (0.26)                                             (0.27)                                       
>         [0.000]                                           [0.000]                               
>             [0.012]                      
R2                                                                                                
>  0.96                                               0.96                                        
>           0.96                                              0.96                                
>               0.95                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
\multicolumn{3}{l}{\textbf{Panel C:} Effect on Total Turnout}                                     
>                                                                                                 
>                                                                                                 
>                                          
\midrule                                                                                          
>                                                                                                 
>                                                                                                 
>                                          
Reassignment (#t-4#)                                                                              
> -0.22                                               -0.22                                       
>          -0.22                                             -0.22                                
>              -0.22                       
                                                                                                  
> (0.17)                                             (0.17)                                       
>         [0.191]                                           [0.221]                               
>             [0.189]                      
Reassignment (#t-3#)                                                                              
> -0.05                                               -0.05                                       
>          -0.05                                             -0.05                                
>              -0.15                       
                                                                                                  
> (0.16)                                             (0.16)                                       
>         [0.743]                                           [0.752]                               
>             [0.375]                      
Reassignment (#t-2#)                                                                              
> -0.05                                               -0.05                                       
>          -0.05                                             -0.05                                
>              -0.01                       
                                                                                                  
> (0.13)                                             (0.14)                                       
>         [0.667]                                           [0.732]                               
>             [0.957]                      
Reassignment (#t+0#)                                                                              
> -0.39*                                             -0.39*                                       
>          -0.39                                             -0.39                                
>              -0.53                       
                                                                                                  
> (0.16)                                             (0.19)                                       
>         [0.025]                                           [0.027]                               
>             [0.003]                      
Reassignment (#t+1#)                                                                              
>  0.01                                               0.01                                        
>           0.01                                              0.01                                
>              -0.00                       
                                                                                                  
> (0.20)                                             (0.21)                                       
>         [0.950]                                           [0.958]                               
>             [0.994]                      
Reassignment (#t+2#)                                                                              
>  0.30                                               0.30                                        
>           0.30                                              0.30                                
>               0.27                       
                                                                                                  
> (0.22)                                             (0.21)                                       
>         [0.187]                                           [0.097]                               
>             [0.408]                      
R2                                                                                                
>  0.99                                               0.99                                        
>           0.99                                              0.99                                
>               0.99                       
N                                                                                                 
> 4,666                                               4,666                                       
>          4,666                                             4,666                                
>              4,666                       
Precinct FE                                                                                       
>   X                                                  X                                          
>            X                                                 X                                  
>                X                         
Election-District FE                                                                              
>   X                                                  X                                          
>            X                                                 X                                  
>                                          
Election-District FE                                                                              
>                                                                                                 
>                                                                                                 
>                                          
Cluster Precinct                                                                                  
>   X                                                                                             
>                                                                                                 
>                                          
Number of Clusters                                                                                
>  618                                               200+618                                      
>           618                                                25                                 
>                25                        
TW Cluster District-Election + Precinct                                                           
>                                                      X                                          
>                                                                                                 
>                                          
WRC Precinct                                                                                      
>                                                                                                 
>            X                                                                                    
>                                          
WRC District                                                                                      
>                                                                                                 
>                                                              X                                  
>                X                         
Election FE                                                                                       
>                                                                                                 
>                                                                                                 
>                X                         
                                           {hline 333}


.                 cleantex "$tables/Table_C2_ES_rob_clustering.tex" , nodisplay   replace

. 
. 
. ********************************************************************************
. *        Heterogeneity: Increase/Decrease Distance [Full Sample] (Table C3)
. ********************************************************************************
.                 
.         * TABLE C3. Effect Heterogeneity by Change in Proximity to the Polling Place
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_dn                            // a 
> := decrease
  3.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b = F`l'event *ind_dist_up                         
>    // b:= increase
  5.                 lab var F`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6. 
.                 assert  F`l'event_b+F`l'event_a==F`l'event              
  7.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_dn    // a := decrease
  3.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b = L`l'event *ind_dist_up    // b:= increase
  5.                 lab var L`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 assert  L`l'event_b+L`l'event_a==L`l'event
  7.         }               

.         
.         // ORDER dummies
.         order *event_b, last

.         order F1event*,last     

.         
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         outreg,clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.         // (1) Baseline: Smpl_trim
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b ///
>                                 $ctr $wgt if smpl_trim ==1, absorb(i.wahl_id#i.stadtbez i.sb_new
> ) cluster(sb_new)
  3.                                  outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b
> ) ctitle("")
  4. 
.         // (2)  CLEAN sample (remove CTRL precincts with some NONZERO reass and TREAT precinct w
> ith 1+ treatment)
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b ///
>                                 $ctr $wgt if cleanctr==1 & fulltottreat100<=1, absorb(i.wahl_id#
> i.stadtbez i.sb_new) cluster(sb_new)
  5.                                  outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b
> ) addrow( Clean sample, X) ctitle("", "\multicolumn{3}{c}{`v'}")
  6. 
.                                 
.         // (2) balanced SAMPLE
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b ///
>                                 $ctr $wgt if smpl_bal==1 & fulltottreat100<=1, absorb(i.wahl_id#
> i.stadtbez i.sb_new) cluster(sb_new)
  7.                                  outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b
> ) addrow(Balanced sample, X) ctitle("")                    
  8.                                                 
.         }
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      14.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9734
                                                  Adj R-squared   =     0.9674
                                                  Within R-sq.    =     0.2120
Number of clusters (sb_new)  =        618         Root MSE        =     1.6565

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .3228052   .5620586     0.57   0.566    -.7809746    1.426585
        F6event_a |   -.008119   .5445209    -0.01   0.988    -1.077458     1.06122
        F5event_a |   .2401916    .384451     0.62   0.532    -.5147995    .9951827
        F4event_a |   -.156618   .2415581    -0.65   0.517    -.6309938    .3177578
        F3event_a |  -.2497959    .237662    -1.05   0.294    -.7165204    .2169286
        F2event_a |  -.2004224   .1774212    -1.13   0.259    -.5488451    .1480002
        L0event_a |    .475771   .3448323     1.38   0.168    -.2014161    1.152958
        L1event_a |   .6029534   .3142192     1.92   0.055    -.0141153    1.220022
        L2event_a |    .489146   .3473889     1.41   0.160     -.193062    1.171354
        L3event_a |    .768641   .3520682     2.18   0.029     .0772438    1.460038
        L4event_a |   .2930804   .6191775     0.47   0.636    -.9228704    1.509031
        L5event_a |   .5968234   1.001193     0.60   0.551    -1.369335    2.562982
        L6event_a |   2.971707   1.125918     2.64   0.009     .7606118    5.182803
        L7event_a |   .4542621    .595416     0.76   0.446    -.7150254     1.62355
        F7event_b |   -.458259   .4061302    -1.13   0.260    -1.255824    .3393062
        F6event_b |   .0370394   .3490613     0.11   0.916    -.6484527    .7225316
        F5event_b |   .1948103   .2958098     0.66   0.510    -.3861058    .7757263
        F4event_b |   .1010004   .2103875     0.48   0.631     -.312162    .5141628
        F3event_b |    .051904   .2046495     0.25   0.800    -.3499901     .453798
        F2event_b |   .1484136   .1455165     1.02   0.308    -.1373541    .4341812
        L0event_b |  -1.892486   .2685797    -7.05   0.000    -2.419927   -1.365045
        L1event_b |  -1.964155   .2722347    -7.21   0.000    -2.498774   -1.429536
        L2event_b |   -1.59154    .310441    -5.13   0.000    -2.201189   -.9818908
        L3event_b |  -1.046249   .3334843    -3.14   0.002    -1.701151   -.3913475
        L4event_b |  -1.513942   .5864355    -2.58   0.010    -2.665594   -.3622908
        L5event_b |  -1.192543   .6858851    -1.74   0.083    -2.539496     .154409
        L6event_b |  -.5363127   .5611732    -0.96   0.340    -1.638354    .5657283
        L7event_b |   1.781413   1.393593     1.28   0.202    -.9553482    4.518174
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -.9961214   .9212525    -1.08   0.280    -2.805292    .8130493
         ew_biodt |   .3683074   .0277532    13.27   0.000     .3138051    .4228097
        ew_dtmihi |   .0672716   .0499711     1.35   0.179    -.0308624    .1654056
         ew_ledig |   .2075291   .0540848     3.84   0.000     .1013165    .3137417
       ew_married |   .4088781   .0556124     7.35   0.000     .2996655    .5180906
        wb_anteil |   -.284197   .0201233   -14.12   0.000    -.3237154   -.2446785
          wb_ausl |    .016981   .0158374     1.07   0.284    -.0141208    .0480829
         wb_18t24 |  -.0166004   .0296128    -0.56   0.575    -.0747545    .0415537
         wb_25t34 |  -.0623142   .0187354    -3.33   0.001    -.0991071   -.0255213
         wb_35t44 |   .0057088   .0225147     0.25   0.800    -.0385059    .0499234
         wb_45t59 |   .0149036   .0214535     0.69   0.488    -.0272271    .0570344
          avg_dur |  -.0226264   .0201538    -1.12   0.262    -.0622046    .0169519
          hh_kids |  -.0391984   .0390032    -1.01   0.315    -.1157936    .0373968
mpreis_flats_rent |   .0286627   .0245902     1.17   0.244     -.019628    .0769534
            _cons |   13.43092   8.531997     1.57   0.116    -3.324354     30.1862
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: no existing table found for merge or append

                       ----------------------------------------------------
                        (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16   
                                                                  (0.24)  
                        (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25   
                                                                  (0.24)  
                        (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20   
                                                                  (0.18)  
                        (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48   
                                                                  (0.34)  
                        (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60   
                                                                  (0.31)  
                        (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49   
                                                                  (0.35)  
                        (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10   
                                                                  (0.21)  
                        (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05   
                                                                  (0.20)  
                        (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15   
                                                                  (0.15)  
                        (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89*** 
                                                                  (0.27)  
                        (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96*** 
                                                                  (0.27)  
                        (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59*** 
                                                                  (0.31)  
                        R2                                         0.97   
                        N                                         4,666   
                       ----------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  42,    254) =      10.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9760
                                                  Adj R-squared   =     0.9683
                                                  Within R-sq.    =     0.2441
Number of clusters (sb_new)  =        255         Root MSE        =     1.6187

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .1581399   .6756431     0.23   0.815    -1.172436    1.488716
        F6event_a |  -.1900103      .6772    -0.28   0.779    -1.523652    1.143632
        F5event_a |   .0891076   .4865509     0.18   0.855    -.8690803    1.047295
        F4event_a |  -.1745887   .3271263    -0.53   0.594    -.8188141    .4696366
        F3event_a |   -.194647   .3203623    -0.61   0.544    -.8255517    .4362576
        F2event_a |  -.1712882   .2610867    -0.66   0.512    -.6854586    .3428822
        L0event_a |   .1254433   .4041835     0.31   0.757    -.6705346    .9214211
        L1event_a |  -.0678497   .3907612    -0.17   0.862    -.8373943    .7016949
        L2event_a |   .1966194   .3966961     0.50   0.621    -.5846131    .9778518
        L3event_a |   .2802897   .4252984     0.66   0.510    -.5572707     1.11785
        L4event_a |  -.4648707   .8671401    -0.54   0.592    -2.172571     1.24283
        L5event_a |   -.256495   1.080559    -0.24   0.813    -2.384492    1.871502
        L6event_a |   2.998776   1.388523     2.16   0.032     .2642918    5.733261
        L7event_a |   .0260003   .8948084     0.03   0.977    -1.736188    1.788189
        F7event_b |  -.7575703   .4736478    -1.60   0.111    -1.690347    .1752068
        F6event_b |  -.1650337   .4087458    -0.40   0.687    -.9699962    .6399289
        F5event_b |   .0554037   .3913302     0.14   0.888    -.7152614    .8260689
        F4event_b |   .1322745   .3011906     0.44   0.661    -.4608743    .7254234
        F3event_b |  -.2135684   .3059891    -0.70   0.486    -.8161674    .3890305
        F2event_b |   .1047146   .2420253     0.43   0.666    -.3719173    .5813465
        L0event_b |  -2.237623   .3786553    -5.91   0.000    -2.983327   -1.491919
        L1event_b |   -2.40491   .3689351    -6.52   0.000    -3.131471   -1.678349
        L2event_b |  -1.984556   .4019507    -4.94   0.000    -2.776137   -1.192976
        L3event_b |  -1.501867   .4308533    -3.49   0.001    -2.350366   -.6533667
        L4event_b |  -2.307491   .8198138    -2.81   0.005    -3.921989    -.692993
        L5event_b |  -2.492512   .8389945    -2.97   0.003    -4.144784   -.8402402
        L6event_b |  -1.767055   1.292577    -1.37   0.173    -4.312589    .7784789
        L7event_b |   1.054092   1.548358     0.68   0.497    -1.995163    4.103347
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -4.694919   1.415197    -3.32   0.001    -7.481934   -1.907905
         ew_biodt |   .3324988   .0449819     7.39   0.000     .2439138    .4210837
        ew_dtmihi |   .0865486   .0771369     1.12   0.263    -.0653607    .2384579
         ew_ledig |    .227589   .0855526     2.66   0.008     .0591062    .3960718
       ew_married |   .3952067    .087539     4.51   0.000     .2228121    .5676013
        wb_anteil |  -.2797433   .0318486    -8.78   0.000    -.3424644   -.2170223
          wb_ausl |   .0233537   .0222569     1.05   0.295    -.0204779    .0671853
         wb_18t24 |  -.0080402   .0389289    -0.21   0.837    -.0847048    .0686244
         wb_25t34 |  -.0457049   .0283684    -1.61   0.108    -.1015721    .0101622
         wb_35t44 |   .0026012   .0331784     0.08   0.938    -.0627387     .067941
         wb_45t59 |   .0225713   .0294372     0.77   0.444    -.0354009    .0805435
          avg_dur |  -.0420584   .0327915    -1.28   0.201    -.1066364    .0225195
          hh_kids |   .0587706   .0522449     1.12   0.262    -.0441178     .161659
mpreis_flats_rent |   .0098483   .0404632     0.24   0.808    -.0698378    .0895344
            _cons |   41.78736   12.81441     3.26   0.001     16.55133    67.02339
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

      --------------------------------------------------------------------------------------
                                                          \multicolumn{3}{c}{turnout_urne} 
      --------------------------------------------------------------------------------------
       (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17               
                                                 (0.24)                (0.33)              
       (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19               
                                                 (0.24)                (0.32)              
       (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17               
                                                 (0.18)                (0.26)              
       (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13               
                                                 (0.34)                (0.40)              
       (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07               
                                                 (0.31)                (0.39)              
       (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20               
                                                 (0.35)                (0.40)              
       (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13               
                                                 (0.21)                (0.30)              
       (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21               
                                                 (0.20)                (0.31)              
       (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10               
                                                 (0.15)                (0.24)              
       (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***             
                                                 (0.27)                (0.38)              
       (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***             
                                                 (0.27)                (0.37)              
       (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***             
                                                 (0.31)                (0.40)              
       R2                                         0.97                  0.98               
       N                                         4,666                 2,040               
       Clean sample                                                      X                 
      --------------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  30,    431) =      15.54
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9758
                                                  Adj R-squared   =     0.9700
                                                  Within R-sq.    =     0.2099
Number of clusters (sb_new)  =        432         Root MSE        =     1.6103

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |   .5211905   .6612172     0.79   0.431    -.7784208    1.820802
        F4event_a |    .024859   .3238628     0.08   0.939     -.611688    .6614059
        F3event_a |    .186852   .3587719     0.52   0.603    -.5183082    .8920122
        F2event_a |   .0853485   .2839568     0.30   0.764     -.472764    .6434609
        L0event_a |   .4122388   .4594521     0.90   0.370    -.4908067    1.315284
        L1event_a |   .4077678   .4287684     0.95   0.342    -.4349693    1.250505
        L2event_a |    .753802   .4239595     1.78   0.076    -.0794834    1.587087
        L3event_a |    .805224   .4181126     1.93   0.055    -.0165692    1.627017
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |   .6023666   .6077144     0.99   0.322    -.5920858    1.796819
        F4event_b |   .0481439   .3128402     0.15   0.878    -.5667383    .6630262
        F3event_b |  -.0940259   .3109917    -0.30   0.763    -.7052748    .5172231
        F2event_b |   .2785513   .2663686     1.05   0.296    -.2449918    .8020944
        L0event_b |  -2.921113   .3893919    -7.50   0.000    -3.686457    -2.15577
        L1event_b |  -2.662371   .3917178    -6.80   0.000    -3.432286   -1.892457
        L2event_b |  -1.878365    .409691    -4.58   0.000    -2.683605   -1.073124
        L3event_b |  -1.062102   .3841721    -2.76   0.006    -1.817186   -.3070179
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -1.153993   1.019794    -1.13   0.258    -3.158381     .850395
         ew_biodt |   .3714819   .0302416    12.28   0.000     .3120426    .4309212
        ew_dtmihi |   .0188454   .0578032     0.33   0.745    -.0947659    .1324567
         ew_ledig |   .1921957    .065699     2.93   0.004     .0630654    .3213259
       ew_married |    .346164   .0668376     5.18   0.000     .2147958    .4775321
        wb_anteil |  -.2748316   .0221296   -12.42   0.000     -.318327   -.2313363
          wb_ausl |   .0301201   .0168658     1.79   0.075    -.0030294    .0632695
         wb_18t24 |  -.0123693   .0365381    -0.34   0.735    -.0841843    .0594457
         wb_25t34 |  -.0519388   .0213533    -2.43   0.015    -.0939084   -.0099692
         wb_35t44 |   .0043876   .0257089     0.17   0.865    -.0461429     .054918
         wb_45t59 |   .0358437   .0229444     1.56   0.119    -.0092532    .0809406
          avg_dur |  -.0292881   .0220401    -1.33   0.185    -.0726077    .0140314
          hh_kids |   .0021702   .0459521     0.05   0.962    -.0881479    .0924883
mpreis_flats_rent |   .0363794   .0281238     1.29   0.197    -.0188974    .0916562
            _cons |   15.97834   9.643718     1.66   0.098    -2.976223    34.93291
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17                  0.02   
                                            (0.24)                (0.33)                (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19                  0.19   
                                            (0.24)                (0.32)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17                  0.09   
                                            (0.18)                (0.26)                (0.28)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13                  0.41   
                                            (0.34)                (0.40)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07                  0.41   
                                            (0.31)                (0.39)                (0.43)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20                  0.75   
                                            (0.35)                (0.40)                (0.42)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13                  0.05   
                                            (0.21)                (0.30)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21                 -0.09   
                                            (0.20)                (0.31)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10                  0.28   
                                            (0.15)                (0.24)                (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***              -2.92*** 
                                            (0.27)                (0.38)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***              -2.66*** 
                                            (0.27)                (0.37)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***              -1.88*** 
                                            (0.31)                (0.40)                (0.41)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------

(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      14.90
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9628
                                                  Adj R-squared   =     0.9544
                                                  Within R-sq.    =     0.2284
Number of clusters (sb_new)  =        618         Root MSE        =     1.6618

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.7726385   .5349539    -1.44   0.149     -1.82319    .2779128
        F6event_a |  -.1638495     .45516    -0.36   0.719      -1.0577    .7300012
        F5event_a |  -1.068847   .4042341    -2.64   0.008    -1.862688   -.2750054
        F4event_a |  -.2732057   .2186614    -1.25   0.212    -.7026165    .1562052
        F3event_a |   .1422311   .2194149     0.65   0.517    -.2886594    .5731216
        F2event_a |  -.0493139    .179667    -0.27   0.784    -.4021469    .3035192
        L0event_a |  -.4599664   .3083689    -1.49   0.136    -1.065546    .1456135
        L1event_a |  -.3946958   .3108984    -1.27   0.205    -1.005243    .2158516
        L2event_a |   .0490141   .3556253     0.14   0.890    -.6493687    .7473968
        L3event_a |  -.6600526   .3220275    -2.05   0.041    -1.292455   -.0276498
        L4event_a |  -.1714753   .9749698    -0.18   0.860    -2.086137    1.743186
        L5event_a |   1.330135   .8766554     1.52   0.130    -.3914553    3.051725
        L6event_a |  -2.832304   .6886765    -4.11   0.000    -4.184738    -1.47987
        L7event_a |  -1.841205   .6807652    -2.70   0.007    -3.178103   -.5043073
        F7event_b |   .5841825   .3298669     1.77   0.077    -.0636156    1.231981
        F6event_b |   .4327823   .2632287     1.64   0.101    -.0841505     .949715
        F5event_b |  -.1844277   .3115985    -0.59   0.554    -.7963498    .4274945
        F4event_b |  -.1933853   .1940332    -1.00   0.319    -.5744308    .1876602
        F3event_b |  -.0668334   .1799966    -0.37   0.711    -.4203135    .2866468
        F2event_b |  -.0883951   .1461363    -0.60   0.545      -.37538    .1985897
        L0event_b |   1.258426   .2583978     4.87   0.000     .7509797    1.765871
        L1event_b |   1.819219   .2672851     6.81   0.000      1.29432    2.344118
        L2event_b |   1.723036   .3262228     5.28   0.000     1.082394    2.363677
        L3event_b |   1.207218   .3296335     3.66   0.000     .5598788    1.854558
        L4event_b |   2.900775    .518063     5.60   0.000     1.883394    3.918155
        L5event_b |    2.93048   .5822414     5.03   0.000     1.787065    4.073896
        L6event_b |   1.816891   .6623496     2.74   0.006      .516158    3.117624
        L7event_b |    .546884   1.228547     0.45   0.656    -1.865757    2.959525
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.622643   1.331899     1.97   0.049      .007038    5.238248
         ew_biodt |    .390918   .0287073    13.62   0.000     .3345422    .4472938
        ew_dtmihi |  -.2363622   .0597435    -3.96   0.000    -.3536875   -.1190369
         ew_ledig |   .2132309   .0770102     2.77   0.006      .061997    .3644649
       ew_married |   .2221102   .0771453     2.88   0.004      .070611    .3736095
        wb_anteil |  -.2460859   .0218023   -11.29   0.000    -.2889017   -.2032702
          wb_ausl |  -.0699769    .014375    -4.87   0.000    -.0982067   -.0417471
         wb_18t24 |  -.0295899   .0273374    -1.08   0.279    -.0832755    .0240957
         wb_25t34 |   .0438136   .0190773     2.30   0.022     .0063493    .0812779
         wb_35t44 |  -.0081487    .024216    -0.34   0.737    -.0557044     .039407
         wb_45t59 |  -.0389038    .020268    -1.92   0.055    -.0787064    .0008987
          avg_dur |   .0429859   .0224788     1.91   0.056    -.0011584    .0871302
          hh_kids |  -.0731669   .0409186    -1.79   0.074    -.1535236    .0071898
mpreis_flats_rent |  -.0170848   .0235218    -0.73   0.468    -.0632773    .0291077
            _cons |  -12.97282   11.12696    -1.17   0.244    -34.82413     8.87849
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17                  0.02   
                                            (0.24)                (0.33)                (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19                  0.19   
                                            (0.24)                (0.32)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17                  0.09   
                                            (0.18)                (0.26)                (0.28)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13                  0.41   
                                            (0.34)                (0.40)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07                  0.41   
                                            (0.31)                (0.39)                (0.43)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20                  0.75   
                                            (0.35)                (0.40)                (0.42)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13                  0.05   
                                            (0.21)                (0.30)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21                 -0.09   
                                            (0.20)                (0.31)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10                  0.28   
                                            (0.15)                (0.24)                (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***              -2.92*** 
                                            (0.27)                (0.38)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***              -2.66*** 
                                            (0.27)                (0.37)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***              -1.88*** 
                                            (0.31)                (0.40)                (0.41)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


                       ---------------------------------------------------
                                                                         
                       ---------------------------------------------------
                        (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.27  
                                                                 (0.22)  
                        (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.14   
                                                                 (0.22)  
                        (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.05  
                                                                 (0.18)  
                        (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.46  
                                                                 (0.31)  
                        (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.39  
                                                                 (0.31)  
                        (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.05   
                                                                 (0.36)  
                        (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.19  
                                                                 (0.19)  
                        (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07  
                                                                 (0.18)  
                        (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.09  
                                                                 (0.15)  
                        (N+)x\hspace{.7cm}Reassignment (#t+0#)   1.26*** 
                                                                 (0.26)  
                        (N+)x\hspace{.7cm}Reassignment (#t+1#)   1.82*** 
                                                                 (0.27)  
                        (N+)x\hspace{.7cm}Reassignment (#t+2#)   1.72*** 
                                                                 (0.33)  
                        R2                                        0.96   
                        N                                         4,666  
                        Clean sample                                     
                        Balanced sample                                  
                       ---------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  42,    254) =      10.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9665
                                                  Adj R-squared   =     0.9558
                                                  Within R-sq.    =     0.2626
Number of clusters (sb_new)  =        255         Root MSE        =     1.6250

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.8291162    .708951    -1.17   0.243    -2.225287    .5670546
        F6event_a |  -.2019318   .5898866    -0.34   0.732    -1.363624    .9597599
        F5event_a |  -.9892084   .5723603    -1.73   0.085    -2.116385     .137968
        F4event_a |  -.2868818   .3272796    -0.88   0.382    -.9314091    .3576455
        F3event_a |   .1721808   .2837223     0.61   0.544    -.3865669    .7309286
        F2event_a |  -.0702658   .2569199    -0.27   0.785    -.5762303    .4356987
        L0event_a |   .1191031      .3929     0.30   0.762    -.6546535    .8928596
        L1event_a |   .3505081   .3809056     0.92   0.358    -.3996275    1.100644
        L2event_a |   .5229487     .44706     1.17   0.243    -.3574678    1.403365
        L3event_a |  -.0353358   .4260802    -0.08   0.934    -.8744358    .8037641
        L4event_a |  -.0161744   1.448468    -0.01   0.991    -2.868711    2.836362
        L5event_a |   2.120988    1.07745     1.97   0.050     -.000886    4.242863
        L6event_a |  -2.639282   1.178074    -2.24   0.026     -4.95932   -.3192438
        L7event_a |  -.6306858   .8566858    -0.74   0.462    -2.317798    1.056426
        F7event_b |   .5471833   .3932756     1.39   0.165    -.2273131     1.32168
        F6event_b |   .5725914   .3215498     1.78   0.076     -.060652    1.205835
        F5event_b |  -.3803916   .4178286    -0.91   0.363    -1.203241    .4424581
        F4event_b |  -.3147656   .3005623    -1.05   0.296    -.9066773     .277146
        F3event_b |   .0277014   .2605219     0.11   0.915    -.4853568    .5407597
        F2event_b |  -.2003538   .2063939    -0.97   0.333    -.6068152    .2061075
        L0event_b |   1.020528   .3500228     2.92   0.004     .3312113    1.709844
        L1event_b |   1.731616   .3631718     4.77   0.000     1.016404    2.446827
        L2event_b |   1.763127   .4234956     4.16   0.000     .9291174    2.597138
        L3event_b |   1.553294   .4183295     3.71   0.000     .7294583    2.377131
        L4event_b |   2.811489   .7362487     3.82   0.000      1.36156    4.261419
        L5event_b |   3.209098     .70836     4.53   0.000     1.814091    4.604105
        L6event_b |   2.479747   1.365711     1.82   0.071    -.2098118    5.169306
        L7event_b |   .9833438   1.630491     0.60   0.547     -2.22766    4.194348
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   6.318574   1.604483     3.94   0.000     3.158788    9.478359
         ew_biodt |   .3949139   .0432804     9.12   0.000     .3096797    .4801481
        ew_dtmihi |  -.1992195   .0780041    -2.55   0.011    -.3528367   -.0456022
         ew_ledig |   .2383878   .0975799     2.44   0.015     .0462192    .4305565
       ew_married |   .3434148   .1047479     3.28   0.001     .1371298    .5496999
        wb_anteil |  -.2333248   .0289615    -8.06   0.000    -.2903601   -.1762896
          wb_ausl |  -.0778922   .0205517    -3.79   0.000    -.1183656   -.0374188
         wb_18t24 |  -.0536685   .0394924    -1.36   0.175    -.1314427    .0241057
         wb_25t34 |   .0039486    .026728     0.15   0.883    -.0486881    .0565852
         wb_35t44 |   -.027556    .035877    -0.77   0.443    -.0982103    .0430983
         wb_45t59 |  -.0393496   .0280113    -1.40   0.161    -.0945136    .0158144
          avg_dur |   .0297291   .0315237     0.94   0.347     -.032352    .0918102
          hh_kids |  -.1864564   .0575744    -3.24   0.001    -.2998404   -.0730724
mpreis_flats_rent |  -.0327005   .0409961    -0.80   0.426    -.1134361    .0480351
            _cons |  -44.71706   14.75737    -3.03   0.003    -73.77945   -15.65467
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17                  0.02   
                                            (0.24)                (0.33)                (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19                  0.19   
                                            (0.24)                (0.32)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17                  0.09   
                                            (0.18)                (0.26)                (0.28)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13                  0.41   
                                            (0.34)                (0.40)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07                  0.41   
                                            (0.31)                (0.39)                (0.43)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20                  0.75   
                                            (0.35)                (0.40)                (0.42)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13                  0.05   
                                            (0.21)                (0.30)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21                 -0.09   
                                            (0.20)                (0.31)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10                  0.28   
                                            (0.15)                (0.24)                (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***              -2.92*** 
                                            (0.27)                (0.38)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***              -2.66*** 
                                            (0.27)                (0.37)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***              -1.88*** 
                                            (0.31)                (0.40)                (0.41)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


     ----------------------------------------------------------------------------------------
                                                        \multicolumn{3}{c}{turnout_pos_req} 
     ----------------------------------------------------------------------------------------
      (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.29                
                                               (0.22)                 (0.33)                
      (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.14                   0.17                 
                                               (0.22)                 (0.28)                
      (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.05                  -0.07                
                                               (0.18)                 (0.26)                
      (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.46                  0.12                 
                                               (0.31)                 (0.39)                
      (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.39                  0.35                 
                                               (0.31)                 (0.38)                
      (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.05                   0.52                 
                                               (0.36)                 (0.45)                
      (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.19                  -0.31                
                                               (0.19)                 (0.30)                
      (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  0.03                 
                                               (0.18)                 (0.26)                
      (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.09                  -0.20                
                                               (0.15)                 (0.21)                
      (N+)x\hspace{.7cm}Reassignment (#t+0#)   1.26***                1.02**                
                                               (0.26)                 (0.35)                
      (N+)x\hspace{.7cm}Reassignment (#t+1#)   1.82***                1.73***               
                                               (0.27)                 (0.36)                
      (N+)x\hspace{.7cm}Reassignment (#t+2#)   1.72***                1.76***               
                                               (0.33)                 (0.42)                
      R2                                        0.96                   0.97                 
      N                                         4,666                  2,040                
      Clean sample                                                       X                  
      Balanced sample                                                                       
     ----------------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  30,    431) =      15.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9635
                                                  Adj R-squared   =     0.9549
                                                  Within R-sq.    =     0.2553
Number of clusters (sb_new)  =        432         Root MSE        =     1.6279

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |  -.3947259   .4164795    -0.95   0.344    -1.213309    .4238575
        F4event_a |  -.1178509   .2665174    -0.44   0.659    -.6416864    .4059846
        F3event_a |  -.0939996   .3201083    -0.29   0.769    -.7231672     .535168
        F2event_a |  -.1950082   .2689378    -0.73   0.469    -.7236009    .3335845
        L0event_a |   .2334228   .4306433     0.54   0.588    -.6129993    1.079845
        L1event_a |   .1515734   .4247009     0.36   0.721    -.6831692    .9863159
        L2event_a |   .4135249   .4810879     0.86   0.391    -.5320454    1.359095
        L3event_a |  -.6598514   .4021453    -1.64   0.102    -1.450261    .1305584
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |  -.1887706   .3362847    -0.56   0.575    -.8497326    .4721914
        F4event_b |  -.2313418     .22723    -1.02   0.309    -.6779587    .2152751
        F3event_b |  -.0123835   .2987876    -0.04   0.967    -.5996454    .5748785
        F2event_b |  -.2668218    .218637    -1.22   0.223    -.6965491    .1629055
        L0event_b |   2.056412   .3618781     5.68   0.000     1.345147    2.767678
        L1event_b |   1.996455   .3848052     5.19   0.000     1.240127    2.752783
        L2event_b |   1.872455   .4270366     4.38   0.000     1.033122    2.711788
        L3event_b |   1.058384   .3934224     2.69   0.007     .2851185    1.831649
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.246547   1.365485     1.65   0.101    -.4372906    4.930384
         ew_biodt |   .4056287   .0308576    13.15   0.000     .3449786    .4662788
        ew_dtmihi |  -.1780644   .0671356    -2.65   0.008    -.3100183   -.0461104
         ew_ledig |   .3024381   .0815232     3.71   0.000     .1422056    .4626705
       ew_married |   .3387201   .0816048     4.15   0.000     .1783273     .499113
        wb_anteil |  -.2557654   .0241522   -10.59   0.000    -.3032361   -.2082947
          wb_ausl |  -.0707518   .0163127    -4.34   0.000    -.1028142   -.0386895
         wb_18t24 |  -.0345468   .0336244    -1.03   0.305    -.1006349    .0315414
         wb_25t34 |   .0387892   .0219566     1.77   0.078    -.0043662    .0819446
         wb_35t44 |  -.0260548   .0277822    -0.94   0.349    -.0806602    .0285507
         wb_45t59 |  -.0525945   .0223954    -2.35   0.019    -.0966123   -.0085768
          avg_dur |   .0327466   .0252149     1.30   0.195    -.0168129    .0823061
          hh_kids |  -.0942543   .0517602    -1.82   0.069    -.1959882    .0074795
mpreis_flats_rent |  -.0129017   .0282284    -0.46   0.648    -.0683842    .0425807
            _cons |  -18.62567   12.17138    -1.53   0.127    -42.54831    5.296966
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17                  0.02   
                                            (0.24)                (0.33)                (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19                  0.19   
                                            (0.24)                (0.32)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17                  0.09   
                                            (0.18)                (0.26)                (0.28)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13                  0.41   
                                            (0.34)                (0.40)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07                  0.41   
                                            (0.31)                (0.39)                (0.43)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20                  0.75   
                                            (0.35)                (0.40)                (0.42)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13                  0.05   
                                            (0.21)                (0.30)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21                 -0.09   
                                            (0.20)                (0.31)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10                  0.28   
                                            (0.15)                (0.24)                (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***              -2.92*** 
                                            (0.27)                (0.38)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***              -2.66*** 
                                            (0.27)                (0.37)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***              -1.88*** 
                                            (0.31)                (0.40)                (0.41)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.29                  -0.12  
                                          (0.22)                 (0.33)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.14                   0.17                   -0.09  
                                          (0.22)                 (0.28)                 (0.32)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.05                  -0.07                  -0.20  
                                          (0.18)                 (0.26)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.46                  0.12                   0.23   
                                          (0.31)                 (0.39)                 (0.43)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.39                  0.35                   0.15   
                                          (0.31)                 (0.38)                 (0.42)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.05                   0.52                   0.41   
                                          (0.36)                 (0.45)                 (0.48)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.19                  -0.31                  -0.23  
                                          (0.19)                 (0.30)                 (0.23)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  0.03                   -0.01  
                                          (0.18)                 (0.26)                 (0.30)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.09                  -0.20                  -0.27  
                                          (0.15)                 (0.21)                 (0.22)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   1.26***                1.02**                 2.06*** 
                                          (0.26)                 (0.35)                 (0.36)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   1.82***                1.73***                2.00*** 
                                          (0.27)                 (0.36)                 (0.38)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   1.72***                1.76***                1.87*** 
                                          (0.33)                 (0.42)                 (0.43)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------

(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      33.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4440
Number of clusters (sb_new)  =        618         Root MSE        =     1.6210

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.4498347   .4925722    -0.91   0.361    -1.417156    .5174865
        F6event_a |  -.1719699   .3902489    -0.44   0.660     -.938347    .5944072
        F5event_a |   -.828655   .4505411    -1.84   0.066    -1.713435    .0561249
        F4event_a |  -.4298239   .2426357    -1.77   0.077    -.9063158     .046668
        F3event_a |  -.1075655   .2168072    -0.50   0.620    -.5333349     .318204
        F2event_a |  -.2497362   .2019212    -1.24   0.217    -.6462722    .1467999
        L0event_a |   .0158041    .239823     0.07   0.947    -.4551642    .4867724
        L1event_a |   .2082574   .2812297     0.74   0.459     -.344026    .7605409
        L2event_a |   .5381604   .3069533     1.75   0.080    -.0646395     1.14096
        L3event_a |   .1085883   .2874196     0.38   0.706     -.455851    .6730276
        L4event_a |   .1216059   .9220699     0.13   0.895     -1.68917    1.932382
        L5event_a |   1.926959   1.282873     1.50   0.134    -.5923686    4.446286
        L6event_a |   .1394028   1.303699     0.11   0.915    -2.420823    2.699628
        L7event_a |  -1.386941   .8623393    -1.61   0.108    -3.080417    .3065348
        F7event_b |   .1259241   .3695883     0.34   0.733    -.5998794    .8517276
        F6event_b |   .4698213   .3243514     1.45   0.148    -.1671453    1.106788
        F5event_b |   .0103831   .2850784     0.04   0.971    -.5494586    .5702248
        F4event_b |  -.0923843    .199051    -0.46   0.643    -.4832838    .2985153
        F3event_b |  -.0149294    .194926    -0.08   0.939    -.3977282    .3678695
        F2event_b |   .0600191   .1591538     0.38   0.706    -.2525298     .372568
        L0event_b |  -.6340596   .2007002    -3.16   0.002    -1.028198   -.2399212
        L1event_b |   -.144935    .252047    -0.58   0.565    -.6399089     .350039
        L2event_b |   .1314959    .278574     0.47   0.637    -.4155722    .6785641
        L3event_b |   .1609696   .3069884     0.52   0.600    -.4418993    .7638384
        L4event_b |   1.386832   .5405588     2.57   0.011     .3252744    2.448391
        L5event_b |   1.737936   .6427991     2.70   0.007     .4755971    3.000276
        L6event_b |   1.280575   .5287144     2.42   0.016     .2422772    2.318873
        L7event_b |   2.328298   .4952053     4.70   0.000     1.355806     3.30079
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.626521   1.066654     1.52   0.128    -.4681905    3.721233
         ew_biodt |   .7592254   .0315323    24.08   0.000     .6973017    .8211491
        ew_dtmihi |  -.1690904   .0518885    -3.26   0.001    -.2709899    -.067191
         ew_ledig |   .4207602   .0705442     5.96   0.000     .2822243    .5592962
       ew_married |   .6309884   .0690977     9.13   0.000     .4952933    .7666835
        wb_anteil |  -.5302829   .0240672   -22.03   0.000    -.5775465   -.4830192
          wb_ausl |  -.0529959   .0176265    -3.01   0.003     -.087611   -.0183807
         wb_18t24 |  -.0461903   .0262697    -1.76   0.079    -.0977791    .0053986
         wb_25t34 |  -.0185006   .0166385    -1.11   0.267    -.0511756    .0141745
         wb_35t44 |  -.0024399   .0210884    -0.12   0.908    -.0438536    .0389737
         wb_45t59 |  -.0240002    .019559    -1.23   0.220    -.0624104    .0144101
          avg_dur |   .0203595   .0222527     0.91   0.361    -.0233406    .0640597
          hh_kids |  -.1123654   .0358261    -3.14   0.002    -.1827212   -.0420096
mpreis_flats_rent |   .0115779   .0234248     0.49   0.621    -.0344241    .0575799
            _cons |   .4580862   9.951597     0.05   0.963    -19.08502     20.0012
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17                  0.02   
                                            (0.24)                (0.33)                (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19                  0.19   
                                            (0.24)                (0.32)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17                  0.09   
                                            (0.18)                (0.26)                (0.28)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13                  0.41   
                                            (0.34)                (0.40)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07                  0.41   
                                            (0.31)                (0.39)                (0.43)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20                  0.75   
                                            (0.35)                (0.40)                (0.42)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13                  0.05   
                                            (0.21)                (0.30)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21                 -0.09   
                                            (0.20)                (0.31)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10                  0.28   
                                            (0.15)                (0.24)                (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***              -2.92*** 
                                            (0.27)                (0.38)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***              -2.66*** 
                                            (0.27)                (0.37)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***              -1.88*** 
                                            (0.31)                (0.40)                (0.41)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.29                  -0.12  
                                          (0.22)                 (0.33)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.14                   0.17                   -0.09  
                                          (0.22)                 (0.28)                 (0.32)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.05                  -0.07                  -0.20  
                                          (0.18)                 (0.26)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.46                  0.12                   0.23   
                                          (0.31)                 (0.39)                 (0.43)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.39                  0.35                   0.15   
                                          (0.31)                 (0.38)                 (0.42)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.05                   0.52                   0.41   
                                          (0.36)                 (0.45)                 (0.48)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.19                  -0.31                  -0.23  
                                          (0.19)                 (0.30)                 (0.23)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  0.03                   -0.01  
                                          (0.18)                 (0.26)                 (0.30)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.09                  -0.20                  -0.27  
                                          (0.15)                 (0.21)                 (0.22)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   1.26***                1.02**                 2.06*** 
                                          (0.26)                 (0.35)                 (0.36)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   1.82***                1.73***                2.00*** 
                                          (0.27)                 (0.36)                 (0.38)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   1.72***                1.76***                1.87*** 
                                          (0.33)                 (0.42)                 (0.43)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------


                       ---------------------------------------------------
                                                                         
                       ---------------------------------------------------
                        (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.43  
                                                                 (0.24)  
                        (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.11  
                                                                 (0.22)  
                        (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.25  
                                                                 (0.20)  
                        (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.02   
                                                                 (0.24)  
                        (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.21   
                                                                 (0.28)  
                        (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.54   
                                                                 (0.31)  
                        (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.09  
                                                                 (0.20)  
                        (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.01  
                                                                 (0.19)  
                        (N+)x\hspace{.7cm}Reassignment (#t-2#)    0.06   
                                                                 (0.16)  
                        (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.63** 
                                                                 (0.20)  
                        (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.14  
                                                                 (0.25)  
                        (N+)x\hspace{.7cm}Reassignment (#t+2#)    0.13   
                                                                 (0.28)  
                        R2                                        0.99   
                        N                                         4,666  
                        Clean sample                                     
                        Balanced sample                                  
                       ---------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  42,    254) =      18.38
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9907
                                                  Adj R-squared   =     0.9877
                                                  Within R-sq.    =     0.4479
Number of clusters (sb_new)  =        255         Root MSE        =     1.6169

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.6709776   .6010701    -1.12   0.265    -1.854694    .5127383
        F6event_a |  -.3919434   .4934486    -0.79   0.428    -1.363715    .5798284
        F5event_a |  -.9000998   .5918248    -1.52   0.130    -2.065609    .2654089
        F4event_a |  -.4614702   .3531509    -1.31   0.192    -1.156947    .2340067
        F3event_a |  -.0224673   .2879967    -0.08   0.938    -.5896328    .5446983
        F2event_a |  -.2415534   .3062015    -0.79   0.431    -.8445705    .3614637
        L0event_a |   .2445457   .3313793     0.74   0.461    -.4080553    .8971466
        L1event_a |   .2826578   .3833264     0.74   0.462    -.4722451    1.037561
        L2event_a |   .7195681   .3710387     1.94   0.054    -.0111359    1.450272
        L3event_a |   .2449531    .368655     0.66   0.507    -.4810568     .970963
        L4event_a |  -.4810436   1.308199    -0.37   0.713    -3.057343    2.095256
        L5event_a |   1.864493    1.33101     1.40   0.162    -.7567287    4.485715
        L6event_a |   .3594948   1.467598     0.24   0.807    -2.530715    3.249705
        L7event_a |   -.604685   .8532952    -0.71   0.479     -2.28512     1.07575
        F7event_b |   -.210387   .4913315    -0.43   0.669    -1.177989    .7572154
        F6event_b |   .4075564   .4261957     0.96   0.340    -.4317711    1.246884
        F5event_b |   -.324988   .4618163    -0.70   0.482    -1.234465    .5844889
        F4event_b |  -.1824906   .2960303    -0.62   0.538    -.7654772     .400496
        F3event_b |  -.1858675   .2886854    -0.64   0.520    -.7543895    .3826544
        F2event_b |  -.0956389   .2401768    -0.40   0.691    -.5686306    .3773528
        L0event_b |  -1.217095   .2994442    -4.06   0.000    -1.806804   -.6273849
        L1event_b |  -.6732935   .3535235    -1.90   0.058    -1.369504    .0229172
        L2event_b |  -.2214297   .3555556    -0.62   0.534    -.9216423    .4787829
        L3event_b |    .051428   .4330409     0.12   0.906    -.8013801    .9042361
        L4event_b |   .5039973   .6769574     0.74   0.457    -.8291672    1.837162
        L5event_b |   .7165839    .579534     1.24   0.217      -.42472    1.857888
        L6event_b |   .7126876   .7684787     0.93   0.355     -.800714    2.226089
        L7event_b |   2.037436   .6837136     2.98   0.003     .6909664    3.383906
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.623651   1.910837     0.85   0.396     -2.13945    5.386753
         ew_biodt |   .7274126   .0522617    13.92   0.000     .6244913     .830334
        ew_dtmihi |  -.1126707   .0795541    -1.42   0.158    -.2693404     .043999
         ew_ledig |   .4659771   .0890018     5.24   0.000     .2907016    .6412525
       ew_married |   .7386217   .0930094     7.94   0.000     .5554539    .9217894
        wb_anteil |  -.5130682   .0348424   -14.73   0.000    -.5816849   -.4444514
          wb_ausl |  -.0545385   .0228372    -2.39   0.018     -.099513   -.0095641
         wb_18t24 |  -.0617087   .0381314    -1.62   0.107    -.1368027    .0133854
         wb_25t34 |  -.0417564   .0280585    -1.49   0.138    -.0970133    .0135006
         wb_35t44 |  -.0249547   .0288412    -0.87   0.388    -.0817531    .0318437
         wb_45t59 |  -.0167783    .028401    -0.59   0.555    -.0727098    .0391532
          avg_dur |  -.0123293   .0373831    -0.33   0.742    -.0859496     .061291
          hh_kids |  -.1276859   .0549567    -2.32   0.021    -.2359148   -.0194569
mpreis_flats_rent |  -.0228522   .0376768    -0.61   0.545    -.0970509    .0513466
            _cons |  -2.929694   16.55043    -0.18   0.860    -35.52325    29.66386
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17                  0.02   
                                            (0.24)                (0.33)                (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19                  0.19   
                                            (0.24)                (0.32)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17                  0.09   
                                            (0.18)                (0.26)                (0.28)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13                  0.41   
                                            (0.34)                (0.40)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07                  0.41   
                                            (0.31)                (0.39)                (0.43)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20                  0.75   
                                            (0.35)                (0.40)                (0.42)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13                  0.05   
                                            (0.21)                (0.30)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21                 -0.09   
                                            (0.20)                (0.31)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10                  0.28   
                                            (0.15)                (0.24)                (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***              -2.92*** 
                                            (0.27)                (0.38)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***              -2.66*** 
                                            (0.27)                (0.37)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***              -1.88*** 
                                            (0.31)                (0.40)                (0.41)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.29                  -0.12  
                                          (0.22)                 (0.33)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.14                   0.17                   -0.09  
                                          (0.22)                 (0.28)                 (0.32)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.05                  -0.07                  -0.20  
                                          (0.18)                 (0.26)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.46                  0.12                   0.23   
                                          (0.31)                 (0.39)                 (0.43)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.39                  0.35                   0.15   
                                          (0.31)                 (0.38)                 (0.42)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.05                   0.52                   0.41   
                                          (0.36)                 (0.45)                 (0.48)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.19                  -0.31                  -0.23  
                                          (0.19)                 (0.30)                 (0.23)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  0.03                   -0.01  
                                          (0.18)                 (0.26)                 (0.30)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.09                  -0.20                  -0.27  
                                          (0.15)                 (0.21)                 (0.22)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   1.26***                1.02**                 2.06*** 
                                          (0.26)                 (0.35)                 (0.36)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   1.82***                1.73***                2.00*** 
                                          (0.27)                 (0.36)                 (0.38)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   1.72***                1.76***                1.87*** 
                                          (0.33)                 (0.42)                 (0.43)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------


     ----------------------------------------------------------------------------------------
                                                        \multicolumn{3}{c}{turnout_tot_req} 
     ----------------------------------------------------------------------------------------
      (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.43                  -0.46                
                                               (0.24)                 (0.35)                
      (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.11                  -0.02                
                                               (0.22)                 (0.29)                
      (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.25                  -0.24                
                                               (0.20)                 (0.31)                
      (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.02                   0.24                 
                                               (0.24)                 (0.33)                
      (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.21                   0.28                 
                                               (0.28)                 (0.38)                
      (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.54                   0.72                 
                                               (0.31)                 (0.37)                
      (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.09                  -0.18                
                                               (0.20)                 (0.30)                
      (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.01                  -0.19                
                                               (0.19)                 (0.29)                
      (N+)x\hspace{.7cm}Reassignment (#t-2#)    0.06                   -0.10                
                                               (0.16)                 (0.24)                
      (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.63**               -1.22***               
                                               (0.20)                 (0.30)                
      (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.14                  -0.67                
                                               (0.25)                 (0.35)                
      (N+)x\hspace{.7cm}Reassignment (#t+2#)    0.13                   -0.22                
                                               (0.28)                 (0.36)                
      R2                                        0.99                   0.99                 
      N                                         4,666                  2,040                
      Clean sample                                                       X                  
      Balanced sample                                                                       
     ----------------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  30,    431) =      39.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9880
                                                  Within R-sq.    =     0.4614
Number of clusters (sb_new)  =        432         Root MSE        =     1.6328

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |   .1264662   .7397831     0.17   0.864    -1.327565    1.580498
        F4event_a |  -.0929914   .3206991    -0.29   0.772    -.7233201    .5373372
        F3event_a |   .0928516    .363129     0.26   0.798    -.6208724    .8065756
        F2event_a |  -.1096588   .3479854    -0.32   0.753    -.7936183    .5743006
        L0event_a |   .6456614   .3448848     1.87   0.062    -.0322039    1.323527
        L1event_a |    .559341   .4083973     1.37   0.172    -.2433572    1.362039
        L2event_a |   1.167327   .3534452     3.30   0.001     .4726368    1.862018
        L3event_a |   .1453728   .3476069     0.42   0.676    -.5378429    .8285884
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |   .4135972    .511283     0.81   0.419     -.591321    1.418515
        F4event_b |  -.1831969   .3134659    -0.58   0.559    -.7993088     .432915
        F3event_b |  -.1064101   .3263177    -0.33   0.745    -.7477821    .5349618
        F2event_b |   .0117296   .2891517     0.04   0.968    -.5565933    .5800525
        L0event_b |  -.8647002    .355324    -2.43   0.015    -1.563084   -.1663167
        L1event_b |  -.6659159   .3748089    -1.78   0.076    -1.402597    .0707647
        L2event_b |  -.0059096   .3566388    -0.02   0.987    -.7068772    .6950579
        L3event_b |  -.0037177   .4052671    -0.01   0.993    -.8002634     .792828
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.092553   1.074355     1.02   0.310    -1.019074    3.204181
         ew_biodt |   .7771106   .0347557    22.36   0.000     .7087989    .8454223
        ew_dtmihi |  -.1592188   .0563688    -2.82   0.005    -.2700107   -.0484269
         ew_ledig |   .4946339   .0708485     6.98   0.000     .3553824    .6338854
       ew_married |   .6848842   .0707442     9.68   0.000     .5458377    .8239308
        wb_anteil |  -.5305971   .0260897   -20.34   0.000    -.5818758   -.4793183
          wb_ausl |  -.0406318   .0213168    -1.91   0.057    -.0825297    .0012661
         wb_18t24 |  -.0469161   .0300273    -1.56   0.119    -.1059341     .012102
         wb_25t34 |  -.0131496   .0199514    -0.66   0.510    -.0523638    .0260646
         wb_35t44 |  -.0216672   .0243474    -0.89   0.374    -.0695216    .0261873
         wb_45t59 |  -.0167508   .0220928    -0.76   0.449    -.0601739    .0266723
          avg_dur |   .0034585   .0252599     0.14   0.891    -.0461895    .0531065
          hh_kids |  -.0920842   .0423983    -2.17   0.030    -.1754174   -.0087511
mpreis_flats_rent |   .0234777   .0278479     0.84   0.400    -.0312569    .0782123
            _cons |  -2.647343   10.53788    -0.25   0.802    -23.35938    18.06469
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17                  0.02   
                                            (0.24)                (0.33)                (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19                  0.19   
                                            (0.24)                (0.32)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17                  0.09   
                                            (0.18)                (0.26)                (0.28)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13                  0.41   
                                            (0.34)                (0.40)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07                  0.41   
                                            (0.31)                (0.39)                (0.43)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20                  0.75   
                                            (0.35)                (0.40)                (0.42)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13                  0.05   
                                            (0.21)                (0.30)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21                 -0.09   
                                            (0.20)                (0.31)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10                  0.28   
                                            (0.15)                (0.24)                (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***              -2.92*** 
                                            (0.27)                (0.38)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***              -2.66*** 
                                            (0.27)                (0.37)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***              -1.88*** 
                                            (0.31)                (0.40)                (0.41)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.29                  -0.12  
                                          (0.22)                 (0.33)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.14                   0.17                   -0.09  
                                          (0.22)                 (0.28)                 (0.32)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.05                  -0.07                  -0.20  
                                          (0.18)                 (0.26)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.46                  0.12                   0.23   
                                          (0.31)                 (0.39)                 (0.43)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.39                  0.35                   0.15   
                                          (0.31)                 (0.38)                 (0.42)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.05                   0.52                   0.41   
                                          (0.36)                 (0.45)                 (0.48)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.19                  -0.31                  -0.23  
                                          (0.19)                 (0.30)                 (0.23)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  0.03                   -0.01  
                                          (0.18)                 (0.26)                 (0.30)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.09                  -0.20                  -0.27  
                                          (0.15)                 (0.21)                 (0.22)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   1.26***                1.02**                 2.06*** 
                                          (0.26)                 (0.35)                 (0.36)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   1.82***                1.73***                2.00*** 
                                          (0.27)                 (0.36)                 (0.38)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   1.72***                1.76***                1.87*** 
                                          (0.33)                 (0.42)                 (0.43)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------


 ------------------------------------------------------------------------------------------------
                                                    \multicolumn{3}{c}{turnout_tot_req}         
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.43                  -0.46                 -0.09  
                                           (0.24)                 (0.35)                 (0.32) 
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.11                  -0.02                  0.09  
                                           (0.22)                 (0.29)                 (0.36) 
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.25                  -0.24                 -0.11  
                                           (0.20)                 (0.31)                 (0.35) 
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.02                   0.24                   0.65  
                                           (0.24)                 (0.33)                 (0.34) 
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.21                   0.28                   0.56  
                                           (0.28)                 (0.38)                 (0.41) 
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.54                   0.72                  1.17** 
                                           (0.31)                 (0.37)                 (0.35) 
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.09                  -0.18                 -0.18  
                                           (0.20)                 (0.30)                 (0.31) 
  (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.01                  -0.19                 -0.11  
                                           (0.19)                 (0.29)                 (0.33) 
  (N+)x\hspace{.7cm}Reassignment (#t-2#)    0.06                   -0.10                  0.01  
                                           (0.16)                 (0.24)                 (0.29) 
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.63**               -1.22***                -0.86* 
                                           (0.20)                 (0.30)                 (0.36) 
  (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.14                  -0.67                 -0.67  
                                           (0.25)                 (0.35)                 (0.37) 
  (N+)x\hspace{.7cm}Reassignment (#t+2#)    0.13                   -0.22                 -0.01  
                                           (0.28)                 (0.36)                 (0.36) 
  R2                                        0.99                   0.99                   0.99  
  N                                         4,666                  2,040                 3,456  
  Clean sample                                                       X                          
  Balanced sample                                                                          X    
 ------------------------------------------------------------------------------------------------


. 
.         
.         // Export Regtable
.         outreg using "$tables/Table_C3_ES_het_by_distance", replay tex replace fragment
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_C3
> _ES_het_by_distance.tex not found)
 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.16                 -0.17                  0.02   
                                            (0.24)                (0.33)                (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.25                 -0.19                  0.19   
                                            (0.24)                (0.32)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.20                 -0.17                  0.09   
                                            (0.18)                (0.26)                (0.28)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.48                  0.13                  0.41   
                                            (0.34)                (0.40)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.60                 -0.07                  0.41   
                                            (0.31)                (0.39)                (0.43)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.49                  0.20                  0.75   
                                            (0.35)                (0.40)                (0.42)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.10                  0.13                  0.05   
                                            (0.21)                (0.30)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.05                 -0.21                 -0.09   
                                            (0.20)                (0.31)                (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15                  0.10                  0.28   
                                            (0.15)                (0.24)                (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -1.89***              -2.24***              -2.92*** 
                                            (0.27)                (0.38)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -1.96***              -2.40***              -2.66*** 
                                            (0.27)                (0.37)                (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.59***              -1.98***              -1.88*** 
                                            (0.31)                (0.40)                (0.41)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.29                  -0.12  
                                          (0.22)                 (0.33)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.14                   0.17                   -0.09  
                                          (0.22)                 (0.28)                 (0.32)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.05                  -0.07                  -0.20  
                                          (0.18)                 (0.26)                 (0.27)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.46                  0.12                   0.23   
                                          (0.31)                 (0.39)                 (0.43)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.39                  0.35                   0.15   
                                          (0.31)                 (0.38)                 (0.42)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.05                   0.52                   0.41   
                                          (0.36)                 (0.45)                 (0.48)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.19                  -0.31                  -0.23  
                                          (0.19)                 (0.30)                 (0.23)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  0.03                   -0.01  
                                          (0.18)                 (0.26)                 (0.30)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.09                  -0.20                  -0.27  
                                          (0.15)                 (0.21)                 (0.22)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   1.26***                1.02**                 2.06*** 
                                          (0.26)                 (0.35)                 (0.36)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   1.82***                1.73***                2.00*** 
                                          (0.27)                 (0.36)                 (0.38)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   1.72***                1.76***                1.87*** 
                                          (0.33)                 (0.42)                 (0.43)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------


 ------------------------------------------------------------------------------------------------
                                                    \multicolumn{3}{c}{turnout_tot_req}         
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.43                  -0.46                 -0.09  
                                           (0.24)                 (0.35)                 (0.32) 
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.11                  -0.02                  0.09  
                                           (0.22)                 (0.29)                 (0.36) 
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.25                  -0.24                 -0.11  
                                           (0.20)                 (0.31)                 (0.35) 
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.02                   0.24                   0.65  
                                           (0.24)                 (0.33)                 (0.34) 
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.21                   0.28                   0.56  
                                           (0.28)                 (0.38)                 (0.41) 
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.54                   0.72                  1.17** 
                                           (0.31)                 (0.37)                 (0.35) 
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.09                  -0.18                 -0.18  
                                           (0.20)                 (0.30)                 (0.31) 
  (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.01                  -0.19                 -0.11  
                                           (0.19)                 (0.29)                 (0.33) 
  (N+)x\hspace{.7cm}Reassignment (#t-2#)    0.06                   -0.10                  0.01  
                                           (0.16)                 (0.24)                 (0.29) 
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.63**               -1.22***                -0.86* 
                                           (0.20)                 (0.30)                 (0.36) 
  (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.14                  -0.67                 -0.67  
                                           (0.25)                 (0.35)                 (0.37) 
  (N+)x\hspace{.7cm}Reassignment (#t+2#)    0.13                   -0.22                 -0.01  
                                           (0.28)                 (0.36)                 (0.36) 
  R2                                        0.99                   0.99                   0.99  
  N                                         4,666                  2,040                 3,456  
  Clean sample                                                       X                          
  Balanced sample                                                                          X    
 ------------------------------------------------------------------------------------------------


.         cleantex "$tables/Table_C3_ES_het_by_distance.tex" , nodisplay  replace

.                 
. 
. ********************************************************************************
. *        Heterogeneity: Distance 3 groups (Table C4)
. ********************************************************************************                
. 
.         * TABLE C4. Effect Heterogeneity by Change in Proximity to the Polling Place (3 bins)
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         
.         // Create two set of dummies: Reason Dummy x rel. time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_m1    // a := decrease
  3.                 gen             F`l'event_b = F`l'event *ind_dist_m2    // b:= middle
  4.                 gen     F`l'event_c = F`l'event *ind_dist_m3    // c := increase
  5.                 assert  F`l'event_b+F`l'event_a+F`l'event_c==F`l'event          
  6.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  7.                 lab var F`l'event_b "(N0)x\hspace{.7cm}Reassignment (#t-`l'#)"
  8.                 lab var F`l'event_c "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  9.                 
.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_m1    // a := decrease
  3.                 gen             L`l'event_b = L`l'event *ind_dist_m2    // b:= middle
  4.                 gen     L`l'event_c = L`l'event *ind_dist_m3    // c := increases
  5.                 assert  L`l'event_b+L`l'event_a+L`l'event_c==L`l'event  
  6.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  7.                 lab var L`l'event_b "(N0)x\hspace{.7cm}Reassignment (#t+`l'#)"
  8.                 lab var L`l'event_c "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"          
  9.                 
.         }               

.         
.         // ORDER dummies
.         order *event_b, last

.         order *event_c, last

.         order F1event*,last     

. 
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         outreg, clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F7event_c-L7event_c F1event
> _a F1event_b F1event_c ///
>                                 $ctr $wgt if smpl_bal==1 & fulltottreat100<=1, absorb(i.wahl_id#
> i.stadtbez i.sb_new) cluster(sb_new)            
  3.                                  outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b
>  F4event_c-L2event_c) store(`v')
  4.                         
. 
.         }       
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F7event_c omitted because of collinearity
note: F6event_c omitted because of collinearity
note: L4event_c omitted because of collinearity
note: L5event_c omitted because of collinearity
note: L6event_c omitted because of collinearity
note: L7event_c omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  38,    431) =      13.36
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9762
                                                  Adj R-squared   =     0.9705
                                                  Within R-sq.    =     0.2233
Number of clusters (sb_new)  =        432         Root MSE        =     1.5988

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |    .853963   .7357979     1.16   0.246    -.5922355    2.300161
        F4event_a |  -.0536046   .3587129    -0.15   0.881    -.7586489    .6514397
        F3event_a |   .0943191   .4027792     0.23   0.815    -.6973367    .8859748
        F2event_a |   .0393432   .3102371     0.13   0.899    -.5704226    .6491089
        L0event_a |   .5635923   .5004761     1.13   0.261    -.4200851     1.54727
        L1event_a |   .4822834   .4640751     1.04   0.299    -.4298485    1.394415
        L2event_a |   1.039139   .4424809     2.35   0.019     .1694501    1.908828
        L3event_a |   .8698303   .4633315     1.88   0.061    -.0408401    1.780501
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |   1.004893   1.032419     0.97   0.331    -1.024311    3.034096
        F4event_b |   .2691916   .4827457     0.56   0.577     -.679637     1.21802
        F3event_b |   .2918869   .4618592     0.63   0.528    -.6158896    1.199663
        F2event_b |   .0803606   .3661522     0.22   0.826    -.6393054    .8000266
        L0event_b |  -1.067781   .5287162    -2.02   0.044    -2.106964   -.0285983
        L1event_b |  -1.063326   .5170055    -2.06   0.040    -2.079492   -.0471605
        L2event_b |  -.3967883   .4367595    -0.91   0.364    -1.255232    .4616553
        L3event_b |   .1411984   .4200648     0.34   0.737    -.6844321    .9668288
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |          0  (omitted)
        F6event_c |          0  (omitted)
        F5event_c |   .1510634   .6875397     0.22   0.826    -1.200284    1.502411
        F4event_c |  -.0031652   .3632877    -0.01   0.993    -.7172012    .7108707
        F3event_c |  -.1966867   .3737674    -0.53   0.599    -.9313203    .5379468
        F2event_c |   .4524144   .3195745     1.42   0.158    -.1757039    1.080533
        L0event_c |  -3.795749   .4419425    -8.59   0.000    -4.664379   -2.927118
        L1event_c |  -3.299938   .4741863    -6.96   0.000    -4.231943   -2.367933
        L2event_c |  -2.681123    .501754    -5.34   0.000    -3.667312   -1.694934
        L3event_c |  -1.597217   .4769933    -3.35   0.001     -2.53474   -.6596947
        L4event_c |          0  (omitted)
        L5event_c |          0  (omitted)
        L6event_c |          0  (omitted)
        L7event_c |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -1.070223   .9881466    -1.08   0.279    -3.012409    .8719626
         ew_biodt |   .3628476    .029862    12.15   0.000     .3041542    .4215409
        ew_dtmihi |   .0039293   .0566596     0.07   0.945    -.1074342    .1152929
         ew_ledig |   .2097037    .063191     3.32   0.001     .0855028    .3339045
       ew_married |   .3571086    .063691     5.61   0.000     .2319251    .4822922
        wb_anteil |  -.2713801   .0222133   -12.22   0.000      -.31504   -.2277202
          wb_ausl |   .0290628     .01697     1.71   0.088    -.0042915     .062417
         wb_18t24 |  -.0089831   .0365436    -0.25   0.806    -.0808089    .0628428
         wb_25t34 |   -.053211    .021248    -2.50   0.013    -.0949737   -.0114484
         wb_35t44 |   .0007671   .0250719     0.03   0.976    -.0485113    .0500455
         wb_45t59 |   .0328642   .0227832     1.44   0.150    -.0119158    .0776441
          avg_dur |  -.0262662   .0219471    -1.20   0.232    -.0694029    .0168705
          hh_kids |  -.0025493   .0457129    -0.06   0.956    -.0923972    .0872985
mpreis_flats_rent |   .0401373   .0277895     1.44   0.149    -.0144825    .0947571
            _cons |   14.65441   9.374522     1.56   0.119     -3.77106    33.07987
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: no existing table found for merge or append

           ---------------------------------------------------------------------------
                                                     Effect on polling place turnout 
           ---------------------------------------------------------------------------
            (N-)x\hspace{.7cm}Reassignment (#t-4#)                -0.05              
                                                                 (0.36)              
            (N-)x\hspace{.7cm}Reassignment (#t-3#)                0.09               
                                                                 (0.40)              
            (N-)x\hspace{.7cm}Reassignment (#t-2#)                0.04               
                                                                 (0.31)              
            (N-)x\hspace{.7cm}Reassignment (#t+0#)                0.56               
                                                                 (0.50)              
            (N-)x\hspace{.7cm}Reassignment (#t+1#)                0.48               
                                                                 (0.46)              
            (N-)x\hspace{.7cm}Reassignment (#t+2#)                1.04*              
                                                                 (0.44)              
            (N0)x\hspace{.7cm}Reassignment (#t-4#)                0.27               
                                                                 (0.48)              
            (N0)x\hspace{.7cm}Reassignment (#t-3#)                0.29               
                                                                 (0.46)              
            (N0)x\hspace{.7cm}Reassignment (#t-2#)                0.08               
                                                                 (0.37)              
            (N0)x\hspace{.7cm}Reassignment (#t+0#)               -1.07*              
                                                                 (0.53)              
            (N0)x\hspace{.7cm}Reassignment (#t+1#)               -1.06*              
                                                                 (0.52)              
            (N0)x\hspace{.7cm}Reassignment (#t+2#)                -0.40              
                                                                 (0.44)              
            (N+)x\hspace{.7cm}Reassignment (#t-4#)                -0.00              
                                                                 (0.36)              
            (N+)x\hspace{.7cm}Reassignment (#t-3#)                -0.20              
                                                                 (0.37)              
            (N+)x\hspace{.7cm}Reassignment (#t-2#)                0.45               
                                                                 (0.32)              
            (N+)x\hspace{.7cm}Reassignment (#t+0#)              -3.80***             
                                                                 (0.44)              
            (N+)x\hspace{.7cm}Reassignment (#t+1#)              -3.30***             
                                                                 (0.47)              
            (N+)x\hspace{.7cm}Reassignment (#t+2#)              -2.68***             
                                                                 (0.50)              
            R2                                                    0.98               
            N                                                     3,456              
           ---------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F7event_c omitted because of collinearity
note: F6event_c omitted because of collinearity
note: L4event_c omitted because of collinearity
note: L5event_c omitted because of collinearity
note: L6event_c omitted because of collinearity
note: L7event_c omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  38,    431) =      14.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9641
                                                  Adj R-squared   =     0.9555
                                                  Within R-sq.    =     0.2674
Number of clusters (sb_new)  =        432         Root MSE        =     1.6169

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |  -.3808435   .5134603    -0.74   0.459    -1.390041    .6283542
        F4event_a |  -.0847917   .2802947    -0.30   0.762    -.6357063     .466123
        F3event_a |   .0964355   .3263304     0.30   0.768    -.5449615    .7378326
        F2event_a |  -.1889865    .285295    -0.66   0.508    -.7497291    .3717561
        L0event_a |   .0282528    .473517     0.06   0.952    -.9024369    .9589426
        L1event_a |   .1087012   .4698859     0.23   0.817    -.8148516    1.032254
        L2event_a |   .0986491   .5077741     0.19   0.846    -.8993724    1.096671
        L3event_a |  -.8202073   .4279367    -1.92   0.056     -1.66131    .0208951
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |  -.7535819   .4553368    -1.65   0.099    -1.648539     .141375
        F4event_b |   -.517395    .369553    -1.40   0.162    -1.243745    .2089553
        F3event_b |  -.1386726   .4887306    -0.28   0.777    -1.099264    .8219193
        F2event_b |     .08392    .354207     0.24   0.813    -.6122679    .7801079
        L0event_b |   .8725722   .4868362     1.79   0.074    -.0842963    1.829441
        L1event_b |   .7434445   .5607407     1.33   0.186     -.358682    1.845571
        L2event_b |      .6816   .5582143     1.22   0.223    -.4155608    1.778761
        L3event_b |   .0027275   .6059024     0.00   0.996    -1.188164    1.193619
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |          0  (omitted)
        F6event_c |          0  (omitted)
        F5event_c |    .167484   .4146618     0.40   0.686    -.6475269    .9824948
        F4event_c |  -.0907304     .25046    -0.36   0.717    -.5830054    .4015445
        F3event_c |  -.1475104   .3384663    -0.44   0.663    -.8127602    .5177394
        F2event_c |  -.5145346   .2548335    -2.02   0.044    -1.015406   -.0136636
        L0event_c |   2.742605   .3973832     6.90   0.000     1.961555    3.523655
        L1event_c |    2.55312   .4273398     5.97   0.000     1.713191     3.39305
        L2event_c |   2.673376   .4857638     5.50   0.000     1.718616    3.628137
        L3event_c |   1.624281   .3914041     4.15   0.000     .8549825    2.393579
        L4event_c |          0  (omitted)
        L5event_c |          0  (omitted)
        L6event_c |          0  (omitted)
        L7event_c |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   2.174794   1.327744     1.64   0.102    -.4348648    4.784453
         ew_biodt |   .4128746   .0301808    13.68   0.000     .3535547    .4721945
        ew_dtmihi |  -.1666574   .0665295    -2.51   0.013      -.29742   -.0358947
         ew_ledig |   .2840727   .0795841     3.57   0.000     .1276515    .4404939
       ew_married |   .3300289   .0791078     4.17   0.000     .1745439    .4855139
        wb_anteil |  -.2595179   .0241442   -10.75   0.000    -.3069729   -.2120628
          wb_ausl |  -.0698814   .0162266    -4.31   0.000    -.1017745   -.0379882
         wb_18t24 |  -.0354172   .0333094    -1.06   0.288    -.1008863    .0300518
         wb_25t34 |   .0394982   .0218578     1.81   0.071    -.0034629    .0824592
         wb_35t44 |  -.0224077   .0271421    -0.83   0.410    -.0757551    .0309396
         wb_45t59 |  -.0508996   .0221147    -2.30   0.022    -.0943656   -.0074336
          avg_dur |   .0295389   .0250855     1.18   0.240    -.0197663     .078844
          hh_kids |  -.0902585   .0513069    -1.76   0.079    -.1911014    .0105844
mpreis_flats_rent |  -.0145485   .0279887    -0.52   0.603    -.0695597    .0404628
            _cons |  -17.28103   11.83406    -1.46   0.145    -40.54069    5.978621
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: no existing table found for merge or append

              ---------------------------------------------------------------------
                                                        Effect on mail-in turnout 
              ---------------------------------------------------------------------
               (N-)x\hspace{.7cm}Reassignment (#t-4#)             -0.08           
                                                                 (0.28)           
               (N-)x\hspace{.7cm}Reassignment (#t-3#)             0.10            
                                                                 (0.33)           
               (N-)x\hspace{.7cm}Reassignment (#t-2#)             -0.19           
                                                                 (0.29)           
               (N-)x\hspace{.7cm}Reassignment (#t+0#)             0.03            
                                                                 (0.47)           
               (N-)x\hspace{.7cm}Reassignment (#t+1#)             0.11            
                                                                 (0.47)           
               (N-)x\hspace{.7cm}Reassignment (#t+2#)             0.10            
                                                                 (0.51)           
               (N0)x\hspace{.7cm}Reassignment (#t-4#)             -0.52           
                                                                 (0.37)           
               (N0)x\hspace{.7cm}Reassignment (#t-3#)             -0.14           
                                                                 (0.49)           
               (N0)x\hspace{.7cm}Reassignment (#t-2#)             0.08            
                                                                 (0.35)           
               (N0)x\hspace{.7cm}Reassignment (#t+0#)             0.87            
                                                                 (0.49)           
               (N0)x\hspace{.7cm}Reassignment (#t+1#)             0.74            
                                                                 (0.56)           
               (N0)x\hspace{.7cm}Reassignment (#t+2#)             0.68            
                                                                 (0.56)           
               (N+)x\hspace{.7cm}Reassignment (#t-4#)             -0.09           
                                                                 (0.25)           
               (N+)x\hspace{.7cm}Reassignment (#t-3#)             -0.15           
                                                                 (0.34)           
               (N+)x\hspace{.7cm}Reassignment (#t-2#)            -0.51*           
                                                                 (0.25)           
               (N+)x\hspace{.7cm}Reassignment (#t+0#)            2.74***          
                                                                 (0.40)           
               (N+)x\hspace{.7cm}Reassignment (#t+1#)            2.55***          
                                                                 (0.43)           
               (N+)x\hspace{.7cm}Reassignment (#t+2#)            2.67***          
                                                                 (0.49)           
               R2                                                 0.96            
               N                                                  3,456           
              ---------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F7event_c omitted because of collinearity
note: F6event_c omitted because of collinearity
note: L4event_c omitted because of collinearity
note: L5event_c omitted because of collinearity
note: L6event_c omitted because of collinearity
note: L7event_c omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  38,    431) =      32.67
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9879
                                                  Within R-sq.    =     0.4612
Number of clusters (sb_new)  =        432         Root MSE        =     1.6355

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |   .4731224   .8526701     0.55   0.579    -1.202786    2.149031
        F4event_a |   -.138396   .3512581    -0.39   0.694    -.8287879    .5519958
        F3event_a |   .1907535   .3826793     0.50   0.618    -.5613963    .9429034
        F2event_a |  -.1496427   .3766901    -0.40   0.691    -.8900208    .5907354
        L0event_a |   .5918446   .3622059     1.63   0.103    -.1200651    1.303754
        L1event_a |   .5909844   .4500481     1.31   0.190    -.2935776    1.475546
        L2event_a |   1.137788    .374032     3.04   0.002     .4026347    1.872942
        L3event_a |   .0496226    .357885     0.14   0.890    -.6537943    .7530395
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |   .2513122   .9176818     0.27   0.784    -1.552376       2.055
        F4event_b |  -.2482019   .5554887    -0.45   0.655    -1.340006    .8436018
        F3event_b |   .1532137   .5210858     0.29   0.769    -.8709718    1.177399
        F2event_b |   .1642809    .499099     0.33   0.742    -.8166899    1.145252
        L0event_b |  -.1952086   .4453701    -0.44   0.661    -1.070576     .680159
        L1event_b |  -.3198805   .5037771    -0.63   0.526    -1.310046    .6702851
        L2event_b |   .2848119   .4569381     0.62   0.533    -.6132923    1.182916
        L3event_b |    .143927   .6023823     0.24   0.811    -1.040045    1.327899
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |          0  (omitted)
        F6event_c |          0  (omitted)
        F5event_c |   .3185478   .5524594     0.58   0.565     -.767302    1.404398
        F4event_c |  -.0938948   .2973813    -0.32   0.752    -.6783927    .4906031
        F3event_c |  -.3441979   .3491473    -0.99   0.325    -1.030441    .3420454
        F2event_c |  -.0621199   .3132568    -0.20   0.843    -.6778209    .5535812
        L0event_c |  -1.053142   .4602268    -2.29   0.023     -1.95771    -.148574
        L1event_c |  -.7468175    .447229    -1.67   0.096    -1.625839    .1322036
        L2event_c |  -.0077473   .4454861    -0.02   0.986    -.8833428    .8678482
        L3event_c |    .027064   .4645747     0.06   0.954    -.8860499    .9401779
        L4event_c |          0  (omitted)
        L5event_c |          0  (omitted)
        L6event_c |          0  (omitted)
        L7event_c |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   1.104571   1.081386     1.02   0.308    -1.020875    3.230017
         ew_biodt |   .7757222   .0348231    22.28   0.000      .707278    .8441664
        ew_dtmihi |  -.1627279   .0565672    -2.88   0.004    -.2739097    -.051546
         ew_ledig |   .4937766   .0714432     6.91   0.000     .3533562     .634197
       ew_married |   .6871377   .0710963     9.66   0.000     .5473992    .8268762
        wb_anteil |   -.530898   .0262068   -20.26   0.000     -.582407   -.4793889
          wb_ausl |  -.0408186   .0213886    -1.91   0.057    -.0828575    .0012202
         wb_18t24 |  -.0444003   .0301785    -1.47   0.142    -.1037156     .014915
         wb_25t34 |  -.0137129   .0200021    -0.69   0.493    -.0530266    .0256009
         wb_35t44 |  -.0216406   .0244929    -0.88   0.377    -.0697809    .0264998
         wb_45t59 |  -.0180354   .0221366    -0.81   0.416    -.0615444    .0254737
          avg_dur |   .0032727   .0253458     0.13   0.897     -.046544    .0530894
          hh_kids |   -.092808   .0423772    -2.19   0.029    -.1760996   -.0095163
mpreis_flats_rent |   .0255888   .0279814     0.91   0.361    -.0294081    .0805857
            _cons |  -2.626643   10.61409    -0.25   0.805    -23.48846    18.23517
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: no existing table found for merge or append

               -------------------------------------------------------------------
                                                         Effect on total turnout 
               -------------------------------------------------------------------
                (N-)x\hspace{.7cm}Reassignment (#t-4#)            -0.14          
                                                                 (0.35)          
                (N-)x\hspace{.7cm}Reassignment (#t-3#)            0.19           
                                                                 (0.38)          
                (N-)x\hspace{.7cm}Reassignment (#t-2#)            -0.15          
                                                                 (0.38)          
                (N-)x\hspace{.7cm}Reassignment (#t+0#)            0.59           
                                                                 (0.36)          
                (N-)x\hspace{.7cm}Reassignment (#t+1#)            0.59           
                                                                 (0.45)          
                (N-)x\hspace{.7cm}Reassignment (#t+2#)           1.14**          
                                                                 (0.37)          
                (N0)x\hspace{.7cm}Reassignment (#t-4#)            -0.25          
                                                                 (0.56)          
                (N0)x\hspace{.7cm}Reassignment (#t-3#)            0.15           
                                                                 (0.52)          
                (N0)x\hspace{.7cm}Reassignment (#t-2#)            0.16           
                                                                 (0.50)          
                (N0)x\hspace{.7cm}Reassignment (#t+0#)            -0.20          
                                                                 (0.45)          
                (N0)x\hspace{.7cm}Reassignment (#t+1#)            -0.32          
                                                                 (0.50)          
                (N0)x\hspace{.7cm}Reassignment (#t+2#)            0.28           
                                                                 (0.46)          
                (N+)x\hspace{.7cm}Reassignment (#t-4#)            -0.09          
                                                                 (0.30)          
                (N+)x\hspace{.7cm}Reassignment (#t-3#)            -0.34          
                                                                 (0.35)          
                (N+)x\hspace{.7cm}Reassignment (#t-2#)            -0.06          
                                                                 (0.31)          
                (N+)x\hspace{.7cm}Reassignment (#t+0#)           -1.05*          
                                                                 (0.46)          
                (N+)x\hspace{.7cm}Reassignment (#t+1#)            -0.75          
                                                                 (0.45)          
                (N+)x\hspace{.7cm}Reassignment (#t+2#)            -0.01          
                                                                 (0.45)          
                R2                                                0.99           
                N                                                 3,456          
               -------------------------------------------------------------------


.         
.         // Estimate ES by 3 distance groups
.         estimates clear

.         outreg,clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.         // (1) Baseline: Smpl_trim
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F7event_c-L7event_c F1event
> _a F1event_b F1event_c ///
>                                 $ctr $wgt if smpl_trim ==1, absorb(i.wahl_id#i.stadtbez i.sb_new
> ) cluster(sb_new)
  3.                                  outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b
>  F4event_c-L2event_c) ctitle("")
  4. 
.         // (2)  CLEAN sample (remove CTRL precincts with some NONZERO reass and TREAT precinct w
> ith 1+ treatment)
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F7event_c-L7event_c F1event
> _a F1event_b F1event_c ///
>                                 $ctr $wgt if cleanctr==1 & fulltottreat100<=1, absorb(i.wahl_id#
> i.stadtbez i.sb_new) cluster(sb_new)
  5.                                  outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b
>  F4event_c-L2event_c) addrow( Clean sample, X) ctitle("", "\multicolumn{3}{c}{`v'}")
  6. 
.                                 
.         // (2) balanced SAMPLE
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F7event_c-L7event_c F1event
> _a F1event_b F1event_c ///
>                                 $ctr $wgt if smpl_bal==1 & fulltottreat100<=1, absorb(i.wahl_id#
> i.stadtbez i.sb_new) cluster(sb_new)
  7.                                  outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b
>  F4event_c-L2event_c) addrow(Balanced sample, X) ctitle("")                        
  8.                                                 
.         }               
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  56,    617) =      17.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9742
                                                  Adj R-squared   =     0.9683
                                                  Within R-sq.    =     0.2358
Number of clusters (sb_new)  =        618         Root MSE        =     1.6343

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   1.017622   .5366414     1.90   0.058    -.0362427    2.071487
        F6event_a |   .6998389   .5337445     1.31   0.190    -.3483372    1.748015
        F5event_a |   .7064358   .4381569     1.61   0.107    -.1540239    1.566895
        F4event_a |  -.0632212   .2816427    -0.22   0.822    -.6163158    .4898733
        F3event_a |  -.2752338   .2849653    -0.97   0.334    -.8348533    .2843856
        F2event_a |  -.1722826    .192511    -0.89   0.371    -.5503389    .2057737
        L0event_a |   .8993293   .4021508     2.24   0.026     .1095789     1.68908
        L1event_a |   .9151499    .357627     2.56   0.011     .2128362    1.617464
        L2event_a |   .8537561   .3772853     2.26   0.024     .1128371    1.594675
        L3event_a |   .9326606   .3927798     2.37   0.018     .1613133    1.704008
        L4event_a |   1.082152   .6620151     1.63   0.103     -.217924    2.382228
        L5event_a |   1.904874   .9186455     2.07   0.039     .1008231    3.708925
        L6event_a |   3.572807   1.110231     3.22   0.001     1.392518    5.753097
        L7event_a |   1.036622   .5927024     1.75   0.081    -.1273364    2.200581
        F7event_b |  -1.157353    .525764    -2.20   0.028    -2.189857   -.1248495
        F6event_b |  -.6697205   .5200319    -1.29   0.198    -1.690968    .3515266
        F5event_b |   .1827155   .3873782     0.47   0.637    -.5780241    .9434551
        F4event_b |   .2149513   .2758557     0.78   0.436    -.3267786    .7566812
        F3event_b |    .004865   .2320969     0.02   0.983    -.4509306    .4606607
        F2event_b |    .008924   .1987647     0.04   0.964    -.3814135    .3992614
        L0event_b |  -.3991258   .3006287    -1.33   0.185    -.9895053    .1912538
        L1event_b |  -.7819364   .3133386    -2.50   0.013    -1.397276   -.1665969
        L2event_b |  -.3839879   .3567626    -1.08   0.282    -1.084604    .3166283
        L3event_b |   .0728486   .3487576     0.21   0.835    -.6120472    .7577445
        L4event_b |  -.8918905   .5953474    -1.50   0.135    -2.061043    .2772624
        L5event_b |  -.8916158   .7581562    -1.18   0.240    -2.380495    .5972636
        L6event_b |   .9451429   .4916294     1.92   0.055    -.0203268    1.910613
        L7event_b |    4.65583   .5272103     8.83   0.000     3.620485    5.691174
        F7event_c |    -.10684   .4727806    -0.23   0.821    -1.035294    .8216143
        F6event_c |   .1210504   .4228581     0.29   0.775    -.7093653    .9514661
        F5event_c |   .0409227   .3646903     0.11   0.911    -.6752619    .7571074
        F4event_c |  -.0721894   .2505257    -0.29   0.773    -.5641758     .419797
        F3event_c |   .0589843   .2639357     0.22   0.823     -.459337    .5773056
        F2event_c |   .1934686   .1834552     1.05   0.292    -.1668037    .5537409
        L0event_c |  -2.806215   .3217508    -8.72   0.000    -3.438075   -2.174356
        L1event_c |  -2.533749   .3440278    -7.36   0.000    -3.209357   -1.858142
        L2event_c |  -2.321825   .3836296    -6.05   0.000    -3.075203   -1.568447
        L3event_c |  -1.612523   .4492884    -3.59   0.000    -2.494843   -.7302031
        L4event_c |  -1.882039   1.039176    -1.81   0.071    -3.922789    .1587105
        L5event_c |  -1.131788   1.119181    -1.01   0.312    -3.329655    1.066079
        L6event_c |  -1.310978   .8097375    -1.62   0.106    -2.901154    .2791979
        L7event_c |  -.6094185    .626483    -0.97   0.331    -1.839716     .620879
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -.8868138   .9024813    -0.98   0.326    -2.659121    .8854936
         ew_biodt |   .3702864   .0274163    13.51   0.000     .3164457     .424127
        ew_dtmihi |   .0622572   .0493268     1.26   0.207    -.0346116    .1591259
         ew_ledig |   .2246273   .0527388     4.26   0.000      .121058    .3281965
       ew_married |   .4053943   .0538047     7.53   0.000     .2997317    .5110569
        wb_anteil |    -.28435   .0198202   -14.35   0.000    -.3232731   -.2454268
          wb_ausl |   .0204514   .0157863     1.30   0.196    -.0105499    .0514527
         wb_18t24 |  -.0033868   .0297567    -0.11   0.909    -.0618235    .0550499
         wb_25t34 |  -.0606317   .0188069    -3.22   0.001     -.097565   -.0236984
         wb_35t44 |   .0050624   .0217557     0.23   0.816    -.0376617    .0477866
         wb_45t59 |     .02102    .021459     0.98   0.328    -.0211215    .0631616
          avg_dur |  -.0204998   .0204824    -1.00   0.317    -.0607235    .0197239
          hh_kids |  -.0494108   .0391753    -1.26   0.208    -.1263439    .0275224
mpreis_flats_rent |   .0244055   .0245096     1.00   0.320     -.023727    .0725379
            _cons |   11.69877   8.421962     1.39   0.165    -4.840411    28.23796
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
warning: no existing table found for merge or append

                       ----------------------------------------------------
                        (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06   
                                                                  (0.28)  
                        (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28   
                                                                  (0.28)  
                        (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17   
                                                                  (0.19)  
                        (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*   
                                                                  (0.40)  
                        (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*   
                                                                  (0.36)  
                        (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*   
                                                                  (0.38)  
                        (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21   
                                                                  (0.28)  
                        (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00   
                                                                  (0.23)  
                        (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01   
                                                                  (0.20)  
                        (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40   
                                                                  (0.30)  
                        (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*  
                                                                  (0.31)  
                        (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38   
                                                                  (0.36)  
                        (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07   
                                                                  (0.25)  
                        (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06   
                                                                  (0.26)  
                        (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19   
                                                                  (0.18)  
                        (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81*** 
                                                                  (0.32)  
                        (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53*** 
                                                                  (0.34)  
                        (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32*** 
                                                                  (0.38)  
                        R2                                         0.97   
                        N                                         4,666   
                       ----------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  56,    254) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.9771
                                                  Adj R-squared   =     0.9694
                                                  Within R-sq.    =     0.2772
Number of clusters (sb_new)  =        255         Root MSE        =     1.5900

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .9131025   .6154435     1.48   0.139    -.2989196    2.125125
        F6event_a |   .6992078   .6468823     1.08   0.281    -.5747283    1.973144
        F5event_a |   .7316175   .5157425     1.42   0.157    -.2840588    1.747294
        F4event_a |  -.0295285   .3661444    -0.08   0.936    -.7505941     .691537
        F3event_a |  -.2191399   .3705407    -0.59   0.555    -.9488633    .5105834
        F2event_a |  -.1931275   .2703252    -0.71   0.476    -.7254917    .3392367
        L0event_a |    .487079   .4540399     1.07   0.284    -.4070834    1.381241
        L1event_a |   .1941391   .4529471     0.43   0.669    -.6978712    1.086149
        L2event_a |   .6152838   .4413543     1.39   0.165    -.2538963    1.484464
        L3event_a |   .4518808   .4697214     0.96   0.337    -.4731638    1.376925
        L4event_a |   .7520087   .9322692     0.81   0.421    -1.083953    2.587971
        L5event_a |   1.234057   .8874047     1.39   0.166    -.5135509    2.981666
        L6event_a |   3.709333   1.408494     2.63   0.009     .9355194    6.483146
        L7event_a |   .6748639   .9091045     0.74   0.459    -1.115479    2.465207
        F7event_b |  -1.542498   .6594577    -2.34   0.020    -2.841199   -.2437963
        F6event_b |   -.966414   .6578862    -1.47   0.143     -2.26202    .3291926
        F5event_b |   .1494401    .591911     0.25   0.801    -1.016238    1.315119
        F4event_b |     .41727   .4681678     0.89   0.374     -.504715    1.339255
        F3event_b |   -.171679   .3985331    -0.43   0.667    -.9565293    .6131712
        F2event_b |  -.0728728   .3241048    -0.22   0.822    -.7111478    .5654023
        L0event_b |  -.8212993   .4593493    -1.79   0.075    -1.725918     .083319
        L1event_b |  -1.226983    .452748    -2.71   0.007    -2.118601   -.3353647
        L2event_b |  -.8411105   .4398532    -1.91   0.057    -1.707334    .0251134
        L3event_b |  -.5165335   .4453199    -1.16   0.247    -1.393523    .3604561
        L4event_b |  -1.434689     .79231    -1.81   0.071    -2.995023    .1256446
        L5event_b |  -1.741806   .8884464    -1.96   0.051    -3.491466    .0078534
        L6event_b |   .7045303   .6287532     1.12   0.264    -.5337033    1.942764
        L7event_b |   3.859101   .6877477     5.61   0.000     2.504687    5.213516
        F7event_c |  -.4199191   .5276918    -0.80   0.427    -1.459128    .6192895
        F6event_c |  -.0297287   .4813952    -0.06   0.951    -.9777633    .9183058
        F5event_c |  -.3487229   .4561219    -0.76   0.445    -1.246985    .5495395
        F4event_c |  -.2058272   .3362802    -0.61   0.541    -.8680798    .4564253
        F3event_c |  -.2044101    .384195    -0.53   0.595    -.9610235    .5522033
        F2event_c |   .2280087   .3031558     0.75   0.453    -.3690105    .8250278
        L0event_c |  -3.105745   .4336999    -7.16   0.000    -3.959851   -2.251639
        L1event_c |  -2.938241   .4654324    -6.31   0.000     -3.85484   -2.021643
        L2event_c |  -2.710769   .5098617    -5.32   0.000    -3.714864   -1.706674
        L3event_c |  -2.008152   .5508458    -3.65   0.000    -3.092959   -.9233453
        L4event_c |  -4.228009   .9665923    -4.37   0.000    -6.131565   -2.324453
        L5event_c |  -4.698042   .9849989    -4.77   0.000    -6.637847   -2.758237
        L6event_c |  -4.233575   1.278432    -3.31   0.001    -6.751252   -1.715898
        L7event_c |  -2.028598   1.152791    -1.76   0.080    -4.298844    .2416472
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   -4.08184   1.445229    -2.82   0.005    -6.927998   -1.235682
         ew_biodt |   .3308766   .0445418     7.43   0.000     .2431582     .418595
        ew_dtmihi |   .0670634    .075591     0.89   0.376    -.0818017    .2159284
         ew_ledig |   .2721497   .0823559     3.30   0.001     .1099624    .4343371
       ew_married |   .4046117   .0807844     5.01   0.000     .2455191    .5637042
        wb_anteil |  -.2747068   .0313933    -8.75   0.000    -.3365312   -.2128825
          wb_ausl |    .026861   .0219046     1.23   0.221    -.0162768    .0699989
         wb_18t24 |   .0049714   .0388333     0.13   0.898     -.071505    .0814477
         wb_25t34 |  -.0535379   .0287044    -1.87   0.063    -.1100668     .002991
         wb_35t44 |  -.0006387   .0318194    -0.02   0.984    -.0633021    .0620247
         wb_45t59 |   .0227008   .0299643     0.76   0.449    -.0363094     .081711
          avg_dur |  -.0363943   .0327672    -1.11   0.268    -.1009242    .0281356
          hh_kids |   .0341592   .0526865     0.65   0.517    -.0695989    .1379172
mpreis_flats_rent |   .0140974   .0393234     0.36   0.720     -.063344    .0915388
            _cons |   34.77893   12.78396     2.72   0.007     9.602865      59.955
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

      --------------------------------------------------------------------------------------
                                                          \multicolumn{3}{c}{turnout_urne} 
      --------------------------------------------------------------------------------------
       (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03               
                                                 (0.28)                (0.37)              
       (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22               
                                                 (0.28)                (0.37)              
       (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19               
                                                 (0.19)                (0.27)              
       (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49               
                                                 (0.40)                (0.45)              
       (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19               
                                                 (0.36)                (0.45)              
       (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62               
                                                 (0.38)                (0.44)              
       (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42               
                                                 (0.28)                (0.47)              
       (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17               
                                                 (0.23)                (0.40)              
       (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07               
                                                 (0.20)                (0.32)              
       (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82               
                                                 (0.30)                (0.46)              
       (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**              
                                                 (0.31)                (0.45)              
       (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84               
                                                 (0.36)                (0.44)              
       (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21               
                                                 (0.25)                (0.34)              
       (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20               
                                                 (0.26)                (0.38)              
       (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23               
                                                 (0.18)                (0.30)              
       (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***             
                                                 (0.32)                (0.43)              
       (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***             
                                                 (0.34)                (0.47)              
       (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***             
                                                 (0.38)                (0.51)              
       R2                                         0.97                  0.98               
       N                                         4,666                 2,040               
       Clean sample                                                      X                 
      --------------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F7event_c omitted because of collinearity
note: F6event_c omitted because of collinearity
note: L4event_c omitted because of collinearity
note: L5event_c omitted because of collinearity
note: L6event_c omitted because of collinearity
note: L7event_c omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  38,    431) =      13.36
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9762
                                                  Adj R-squared   =     0.9705
                                                  Within R-sq.    =     0.2233
Number of clusters (sb_new)  =        432         Root MSE        =     1.5988

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |    .853963   .7357979     1.16   0.246    -.5922355    2.300161
        F4event_a |  -.0536046   .3587129    -0.15   0.881    -.7586489    .6514397
        F3event_a |   .0943191   .4027792     0.23   0.815    -.6973367    .8859748
        F2event_a |   .0393432   .3102371     0.13   0.899    -.5704226    .6491089
        L0event_a |   .5635923   .5004761     1.13   0.261    -.4200851     1.54727
        L1event_a |   .4822834   .4640751     1.04   0.299    -.4298485    1.394415
        L2event_a |   1.039139   .4424809     2.35   0.019     .1694501    1.908828
        L3event_a |   .8698303   .4633315     1.88   0.061    -.0408401    1.780501
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |   1.004893   1.032419     0.97   0.331    -1.024311    3.034096
        F4event_b |   .2691916   .4827457     0.56   0.577     -.679637     1.21802
        F3event_b |   .2918869   .4618592     0.63   0.528    -.6158896    1.199663
        F2event_b |   .0803606   .3661522     0.22   0.826    -.6393054    .8000266
        L0event_b |  -1.067781   .5287162    -2.02   0.044    -2.106964   -.0285983
        L1event_b |  -1.063326   .5170055    -2.06   0.040    -2.079492   -.0471605
        L2event_b |  -.3967883   .4367595    -0.91   0.364    -1.255232    .4616553
        L3event_b |   .1411984   .4200648     0.34   0.737    -.6844321    .9668288
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |          0  (omitted)
        F6event_c |          0  (omitted)
        F5event_c |   .1510634   .6875397     0.22   0.826    -1.200284    1.502411
        F4event_c |  -.0031652   .3632877    -0.01   0.993    -.7172012    .7108707
        F3event_c |  -.1966867   .3737674    -0.53   0.599    -.9313203    .5379468
        F2event_c |   .4524144   .3195745     1.42   0.158    -.1757039    1.080533
        L0event_c |  -3.795749   .4419425    -8.59   0.000    -4.664379   -2.927118
        L1event_c |  -3.299938   .4741863    -6.96   0.000    -4.231943   -2.367933
        L2event_c |  -2.681123    .501754    -5.34   0.000    -3.667312   -1.694934
        L3event_c |  -1.597217   .4769933    -3.35   0.001     -2.53474   -.6596947
        L4event_c |          0  (omitted)
        L5event_c |          0  (omitted)
        L6event_c |          0  (omitted)
        L7event_c |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -1.070223   .9881466    -1.08   0.279    -3.012409    .8719626
         ew_biodt |   .3628476    .029862    12.15   0.000     .3041542    .4215409
        ew_dtmihi |   .0039293   .0566596     0.07   0.945    -.1074342    .1152929
         ew_ledig |   .2097037    .063191     3.32   0.001     .0855028    .3339045
       ew_married |   .3571086    .063691     5.61   0.000     .2319251    .4822922
        wb_anteil |  -.2713801   .0222133   -12.22   0.000      -.31504   -.2277202
          wb_ausl |   .0290628     .01697     1.71   0.088    -.0042915     .062417
         wb_18t24 |  -.0089831   .0365436    -0.25   0.806    -.0808089    .0628428
         wb_25t34 |   -.053211    .021248    -2.50   0.013    -.0949737   -.0114484
         wb_35t44 |   .0007671   .0250719     0.03   0.976    -.0485113    .0500455
         wb_45t59 |   .0328642   .0227832     1.44   0.150    -.0119158    .0776441
          avg_dur |  -.0262662   .0219471    -1.20   0.232    -.0694029    .0168705
          hh_kids |  -.0025493   .0457129    -0.06   0.956    -.0923972    .0872985
mpreis_flats_rent |   .0401373   .0277895     1.44   0.149    -.0144825    .0947571
            _cons |   14.65441   9.374522     1.56   0.119     -3.77106    33.07987
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03                 -0.05   
                                            (0.28)                (0.37)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22                  0.09   
                                            (0.28)                (0.37)                (0.40)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19                  0.04   
                                            (0.19)                (0.27)                (0.31)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49                  0.56   
                                            (0.40)                (0.45)                (0.50)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19                  0.48   
                                            (0.36)                (0.45)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62                 1.04*   
                                            (0.38)                (0.44)                (0.44)  
  (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42                  0.27   
                                            (0.28)                (0.47)                (0.48)  
  (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17                  0.29   
                                            (0.23)                (0.40)                (0.46)  
  (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07                  0.08   
                                            (0.20)                (0.32)                (0.37)  
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82                 -1.07*  
                                            (0.30)                (0.46)                (0.53)  
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**                -1.06*  
                                            (0.31)                (0.45)                (0.52)  
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84                 -0.40   
                                            (0.36)                (0.44)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21                 -0.00   
                                            (0.25)                (0.34)                (0.36)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20                 -0.20   
                                            (0.26)                (0.38)                (0.37)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23                  0.45   
                                            (0.18)                (0.30)                (0.32)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***              -3.80*** 
                                            (0.32)                (0.43)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***              -3.30*** 
                                            (0.34)                (0.47)                (0.47)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***              -2.68*** 
                                            (0.38)                (0.51)                (0.50)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------

(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  56,    617) =      16.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9638
                                                  Adj R-squared   =     0.9555
                                                  Within R-sq.    =     0.2493
Number of clusters (sb_new)  =        618         Root MSE        =     1.6421

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -1.559129   .4596495    -3.39   0.001    -2.461796   -.6564622
        F6event_a |  -.8702494   .3679845    -2.36   0.018    -1.592903   -.1475954
        F5event_a |   -1.59304   .3818414    -4.17   0.000    -2.342906   -.8431733
        F4event_a |  -.3445564   .2387271    -1.44   0.149    -.8133726    .1242597
        F3event_a |   .2294548   .2436625     0.94   0.347    -.2490536    .7079632
        F2event_a |   -.111889   .2040947    -0.55   0.584    -.5126936    .2889155
        L0event_a |  -.6960456    .356652    -1.95   0.051    -1.396445    .0043533
        L1event_a |  -.5842502    .346639    -1.69   0.092    -1.264985     .096485
        L2event_a |  -.2646669   .3804157    -0.70   0.487    -1.011733    .4823996
        L3event_a |  -.8721279   .3503693    -2.49   0.013    -1.560189    -.184067
        L4event_a |  -1.050163   1.106086    -0.95   0.343    -3.222312    1.121986
        L5event_a |   1.008251   1.180946     0.85   0.394     -1.31091    3.327412
        L6event_a |  -3.198605   .7653624    -4.18   0.000    -4.701636   -1.695574
        L7event_a |   -2.18743    .694264    -3.15   0.002    -3.550837    -.824023
        F7event_b |   .6042734   .4920403     1.23   0.220    -.3620033     1.57055
        F6event_b |   .9826422   .4095074     2.40   0.017     .1784449    1.786839
        F5event_b |  -.8015146    .431034    -1.86   0.063    -1.647986     .044957
        F4event_b |  -.4888333   .2646757    -1.85   0.065    -1.008608    .0309412
        F3event_b |   .0071009    .263151     0.03   0.978    -.5096794    .5238811
        F2event_b |  -.1371056   .1953022    -0.70   0.483    -.5206432     .246432
        L0event_b |  -.0535669   .3031019    -0.18   0.860    -.6488033    .5416695
        L1event_b |   .4829375     .34113     1.42   0.157    -.1869792    1.152854
        L2event_b |   .2805788   .4076302     0.69   0.492    -.5199321     1.08109
        L3event_b |  -.0217239    .475521    -0.05   0.964    -.9555597    .9121119
        L4event_b |    2.16665   .6049169     3.58   0.000     .9787042    3.354595
        L5event_b |    1.78515   .6607448     2.70   0.007     .4875691    3.082732
        L6event_b |  -.1987419   .5234973    -0.38   0.704    -1.226794    .8293107
        L7event_b |  -2.302825   .5870697    -3.92   0.000    -3.455722   -1.149929
        F7event_c |   .8030678    .368596     2.18   0.030     .0792129    1.526923
        F6event_c |    .369296   .3066745     1.20   0.229    -.2329564    .9715483
        F5event_c |    .366508    .362693     1.01   0.313    -.3457543     1.07877
        F4event_c |   .0409174   .2215207     0.18   0.854    -.3941085    .4759434
        F3event_c |  -.1311407   .2100045    -0.62   0.533     -.543551    .2812696
        F2event_c |   -.013527   .1821977    -0.07   0.941    -.3713297    .3442758
        L0event_c |   2.041827    .298943     6.83   0.000     1.454758    2.628896
        L1event_c |   2.513704   .3101521     8.10   0.000     1.904622    3.122785
        L2event_c |   2.675139   .3808925     7.02   0.000     1.927136    3.423142
        L3event_c |   1.913533   .3638673     5.26   0.000     1.198964    2.628102
        L4event_c |   2.791987   .6181384     4.52   0.000     1.578077    4.005897
        L5event_c |    3.88078   .9595826     4.04   0.000     1.996337    5.765224
        L6event_c |   3.141249   .6297789     4.99   0.000     1.904479    4.378019
        L7event_c |   2.854822   .5827075     4.90   0.000     1.710491    3.999152
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   2.554313   1.307615     1.95   0.051    -.0136023    5.122228
         ew_biodt |   .3930139   .0279551    14.06   0.000     .3381153    .4479126
        ew_dtmihi |  -.2300266   .0584104    -3.94   0.000    -.3447338   -.1153194
         ew_ledig |   .1939967   .0725282     2.67   0.008     .0515647    .3364287
       ew_married |   .2247479   .0727447     3.09   0.002     .0818906    .3676052
        wb_anteil |  -.2475989   .0214305   -11.55   0.000    -.2896844   -.2055133
          wb_ausl |  -.0714843   .0144493    -4.95   0.000    -.0998601   -.0431084
         wb_18t24 |    -.03851   .0271986    -1.42   0.157     -.091923     .014903
         wb_25t34 |   .0427854   .0188646     2.27   0.024     .0057387     .079832
         wb_35t44 |  -.0055117    .023357    -0.24   0.814    -.0513807    .0403572
         wb_45t59 |  -.0440684   .0198865    -2.22   0.027    -.0831218   -.0050151
          avg_dur |    .038419   .0223518     1.72   0.086    -.0054758    .0823138
          hh_kids |  -.0646407   .0407355    -1.59   0.113    -.1446377    .0153564
mpreis_flats_rent |   -.012461   .0231708    -0.54   0.591    -.0579642    .0330422
            _cons |  -11.62911   10.86766    -1.07   0.285     -32.9712    9.712974
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03                 -0.05   
                                            (0.28)                (0.37)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22                  0.09   
                                            (0.28)                (0.37)                (0.40)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19                  0.04   
                                            (0.19)                (0.27)                (0.31)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49                  0.56   
                                            (0.40)                (0.45)                (0.50)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19                  0.48   
                                            (0.36)                (0.45)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62                 1.04*   
                                            (0.38)                (0.44)                (0.44)  
  (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42                  0.27   
                                            (0.28)                (0.47)                (0.48)  
  (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17                  0.29   
                                            (0.23)                (0.40)                (0.46)  
  (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07                  0.08   
                                            (0.20)                (0.32)                (0.37)  
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82                 -1.07*  
                                            (0.30)                (0.46)                (0.53)  
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**                -1.06*  
                                            (0.31)                (0.45)                (0.52)  
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84                 -0.40   
                                            (0.36)                (0.44)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21                 -0.00   
                                            (0.25)                (0.34)                (0.36)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20                 -0.20   
                                            (0.26)                (0.38)                (0.37)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23                  0.45   
                                            (0.18)                (0.30)                (0.32)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***              -3.80*** 
                                            (0.32)                (0.43)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***              -3.30*** 
                                            (0.34)                (0.47)                (0.47)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***              -2.68*** 
                                            (0.38)                (0.51)                (0.50)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


                       ---------------------------------------------------
                                                                         
                       ---------------------------------------------------
                        (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.34  
                                                                 (0.24)  
                        (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.23   
                                                                 (0.24)  
                        (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.11  
                                                                 (0.20)  
                        (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.70  
                                                                 (0.36)  
                        (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.58  
                                                                 (0.35)  
                        (N-)x\hspace{.7cm}Reassignment (#t+2#)    -0.26  
                                                                 (0.38)  
                        (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.49  
                                                                 (0.26)  
                        (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01   
                                                                 (0.26)  
                        (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.14  
                                                                 (0.20)  
                        (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.05  
                                                                 (0.30)  
                        (N0)x\hspace{.7cm}Reassignment (#t+1#)    0.48   
                                                                 (0.34)  
                        (N0)x\hspace{.7cm}Reassignment (#t+2#)    0.28   
                                                                 (0.41)  
                        (N+)x\hspace{.7cm}Reassignment (#t-4#)    0.04   
                                                                 (0.22)  
                        (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.13  
                                                                 (0.21)  
                        (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.01  
                                                                 (0.18)  
                        (N+)x\hspace{.7cm}Reassignment (#t+0#)   2.04*** 
                                                                 (0.30)  
                        (N+)x\hspace{.7cm}Reassignment (#t+1#)   2.51*** 
                                                                 (0.31)  
                        (N+)x\hspace{.7cm}Reassignment (#t+2#)   2.68*** 
                                                                 (0.38)  
                        R2                                        0.96   
                        N                                         4,666  
                        Clean sample                                     
                        Balanced sample                                  
                       ---------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  56,    254) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.9681
                                                  Adj R-squared   =     0.9575
                                                  Within R-sq.    =     0.2964
Number of clusters (sb_new)  =        255         Root MSE        =     1.5946

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -1.779516   .5480096    -3.25   0.001    -2.858738   -.7002951
        F6event_a |   -1.10568   .4343785    -2.55   0.012    -1.961122   -.2502379
        F5event_a |   -1.76198   .5329414    -3.31   0.001    -2.811527   -.7124329
        F4event_a |  -.4920746   .3227692    -1.52   0.129    -1.127719    .1435702
        F3event_a |   .2508453   .2945527     0.85   0.395    -.3292313     .830922
        F2event_a |  -.1103339   .2766441    -0.40   0.690    -.6551422    .4344744
        L0event_a |  -.2336368   .4429489    -0.53   0.598    -1.105957    .6386835
        L1event_a |   .1014327   .4335603     0.23   0.815    -.7523983    .9552637
        L2event_a |   .0679918   .4816251     0.14   0.888    -.8804954    1.016479
        L3event_a |  -.3527566   .4579267    -0.77   0.442    -1.254573    .5490602
        L4event_a |  -1.775685   1.681291    -1.06   0.292    -5.086732    1.535362
        L5event_a |    1.57736   1.522225     1.04   0.301    -1.420429     4.57515
        L6event_a |  -3.252497   1.229923    -2.64   0.009    -5.674643   -.8303509
        L7event_a |  -1.186505    .917513    -1.29   0.197    -2.993407    .6203966
        F7event_b |   .7520168   .6444684     1.17   0.244    -.5171655    2.021199
        F6event_b |   1.003059   .5534129     1.81   0.071    -.0868038    2.092921
        F5event_b |  -.9928424   .6083599    -1.63   0.104    -2.190915    .2052297
        F4event_b |   -.937132   .4082453    -2.30   0.023    -1.741109   -.1331552
        F3event_b |  -.1114602   .3865324    -0.29   0.773    -.8726768    .6497564
        F2event_b |  -.0382918   .3155083    -0.12   0.903    -.6596374    .5830537
        L0event_b |   .1415727   .4470305     0.32   0.752    -.7387856    1.021931
        L1event_b |   .7982649   .5253145     1.52   0.130    -.2362621    1.832792
        L2event_b |   .6008901   .5784546     1.04   0.300     -.538288    1.740068
        L3event_b |   .6934457   .5861548     1.18   0.238    -.4608968    1.847788
        L4event_b |   2.002206   .8886324     2.25   0.025       .25218    3.752232
        L5event_b |   2.339679   .7497148     3.12   0.002     .8632298    3.816128
        L6event_b |   .0805942   .6836551     0.12   0.906     -1.26576    1.426949
        L7event_b |  -1.728095   .8053558    -2.15   0.033    -3.314121   -.1420699
        F7event_c |   .8526796   .3926586     2.17   0.031     .0793983    1.625961
        F6event_c |   .6544266   .3354231     1.95   0.052     -.006138    1.314991
        F5event_c |   .4166573   .4821428     0.86   0.388    -.5328494    1.366164
        F4event_c |   .2910983   .3467383     0.84   0.402    -.3917499    .9739464
        F3event_c |   .0922393   .3130809     0.29   0.769    -.5243258    .7088045
        F2event_c |  -.2836442    .246945    -1.15   0.252    -.7699647    .2026763
        L0event_c |   1.746758   .3837168     4.55   0.000     .9910868     2.50243
        L1event_c |   2.312645   .4189492     5.52   0.000     1.487588    3.137701
        L2event_c |   2.727277   .4772349     5.71   0.000     1.787435    3.667118
        L3event_c |   2.120411   .4718981     4.49   0.000     1.191079    3.049742
        L4event_c |   4.380138   1.007906     4.35   0.000      2.39522    6.365056
        L5event_c |    5.03874   1.339982     3.76   0.000      2.39985     7.67763
        L6event_c |   5.080334   1.023831     4.96   0.000     3.064054    7.096614
        L7event_c |   4.102188   1.473012     2.78   0.006     1.201316    7.003061
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |    6.02108    1.58214     3.81   0.000     2.905297    9.136863
         ew_biodt |   .4006152   .0418667     9.57   0.000     .3181653    .4830652
        ew_dtmihi |  -.1851749   .0765476    -2.42   0.016    -.3359236   -.0344262
         ew_ledig |   .2013471   .0947098     2.13   0.034     .0148306    .3878635
       ew_married |   .3404693   .1003932     3.39   0.001     .1427602    .5381784
        wb_anteil |  -.2391347   .0274781    -8.70   0.000    -.2932486   -.1850208
          wb_ausl |  -.0803104   .0201405    -3.99   0.000     -.119974   -.0406468
         wb_18t24 |  -.0632949    .038624    -1.64   0.103    -.1393591    .0127692
         wb_25t34 |   .0066794   .0265207     0.25   0.801    -.0455491    .0589078
         wb_35t44 |  -.0235531   .0344726    -0.68   0.495    -.0914417    .0443355
         wb_45t59 |  -.0377198   .0273712    -1.38   0.169    -.0916231    .0161836
          avg_dur |   .0206404   .0302729     0.68   0.496    -.0389775    .0802583
          hh_kids |  -.1716452   .0561654    -3.06   0.002    -.2822544    -.061036
mpreis_flats_rent |  -.0351704    .039587    -0.89   0.375     -.113131    .0427902
            _cons |  -40.67563   14.48406    -2.81   0.005    -69.19977   -12.15149
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03                 -0.05   
                                            (0.28)                (0.37)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22                  0.09   
                                            (0.28)                (0.37)                (0.40)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19                  0.04   
                                            (0.19)                (0.27)                (0.31)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49                  0.56   
                                            (0.40)                (0.45)                (0.50)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19                  0.48   
                                            (0.36)                (0.45)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62                 1.04*   
                                            (0.38)                (0.44)                (0.44)  
  (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42                  0.27   
                                            (0.28)                (0.47)                (0.48)  
  (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17                  0.29   
                                            (0.23)                (0.40)                (0.46)  
  (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07                  0.08   
                                            (0.20)                (0.32)                (0.37)  
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82                 -1.07*  
                                            (0.30)                (0.46)                (0.53)  
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**                -1.06*  
                                            (0.31)                (0.45)                (0.52)  
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84                 -0.40   
                                            (0.36)                (0.44)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21                 -0.00   
                                            (0.25)                (0.34)                (0.36)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20                 -0.20   
                                            (0.26)                (0.38)                (0.37)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23                  0.45   
                                            (0.18)                (0.30)                (0.32)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***              -3.80*** 
                                            (0.32)                (0.43)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***              -3.30*** 
                                            (0.34)                (0.47)                (0.47)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***              -2.68*** 
                                            (0.38)                (0.51)                (0.50)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


     ----------------------------------------------------------------------------------------
                                                        \multicolumn{3}{c}{turnout_pos_req} 
     ----------------------------------------------------------------------------------------
      (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.34                  -0.49                
                                               (0.24)                 (0.32)                
      (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.23                   0.25                 
                                               (0.24)                 (0.29)                
      (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.11                  -0.11                
                                               (0.20)                 (0.28)                
      (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.70                  -0.23                
                                               (0.36)                 (0.44)                
      (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.58                  0.10                 
                                               (0.35)                 (0.43)                
      (N-)x\hspace{.7cm}Reassignment (#t+2#)    -0.26                  0.07                 
                                               (0.38)                 (0.48)                
      (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.49                 -0.94*                
                                               (0.26)                 (0.41)                
      (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.11                
                                               (0.26)                 (0.39)                
      (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.14                  -0.04                
                                               (0.20)                 (0.32)                
      (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.05                  0.14                 
                                               (0.30)                 (0.45)                
      (N0)x\hspace{.7cm}Reassignment (#t+1#)    0.48                   0.80                 
                                               (0.34)                 (0.53)                
      (N0)x\hspace{.7cm}Reassignment (#t+2#)    0.28                   0.60                 
                                               (0.41)                 (0.58)                
      (N+)x\hspace{.7cm}Reassignment (#t-4#)    0.04                   0.29                 
                                               (0.22)                 (0.35)                
      (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.13                  0.09                 
                                               (0.21)                 (0.31)                
      (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.01                  -0.28                
                                               (0.18)                 (0.25)                
      (N+)x\hspace{.7cm}Reassignment (#t+0#)   2.04***                1.75***               
                                               (0.30)                 (0.38)                
      (N+)x\hspace{.7cm}Reassignment (#t+1#)   2.51***                2.31***               
                                               (0.31)                 (0.42)                
      (N+)x\hspace{.7cm}Reassignment (#t+2#)   2.68***                2.73***               
                                               (0.38)                 (0.48)                
      R2                                        0.96                   0.97                 
      N                                         4,666                  2,040                
      Clean sample                                                       X                  
      Balanced sample                                                                       
     ----------------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F7event_c omitted because of collinearity
note: F6event_c omitted because of collinearity
note: L4event_c omitted because of collinearity
note: L5event_c omitted because of collinearity
note: L6event_c omitted because of collinearity
note: L7event_c omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  38,    431) =      14.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9641
                                                  Adj R-squared   =     0.9555
                                                  Within R-sq.    =     0.2674
Number of clusters (sb_new)  =        432         Root MSE        =     1.6169

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |  -.3808435   .5134603    -0.74   0.459    -1.390041    .6283542
        F4event_a |  -.0847917   .2802947    -0.30   0.762    -.6357063     .466123
        F3event_a |   .0964355   .3263304     0.30   0.768    -.5449615    .7378326
        F2event_a |  -.1889865    .285295    -0.66   0.508    -.7497291    .3717561
        L0event_a |   .0282528    .473517     0.06   0.952    -.9024369    .9589426
        L1event_a |   .1087012   .4698859     0.23   0.817    -.8148516    1.032254
        L2event_a |   .0986491   .5077741     0.19   0.846    -.8993724    1.096671
        L3event_a |  -.8202073   .4279367    -1.92   0.056     -1.66131    .0208951
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |  -.7535819   .4553368    -1.65   0.099    -1.648539     .141375
        F4event_b |   -.517395    .369553    -1.40   0.162    -1.243745    .2089553
        F3event_b |  -.1386726   .4887306    -0.28   0.777    -1.099264    .8219193
        F2event_b |     .08392    .354207     0.24   0.813    -.6122679    .7801079
        L0event_b |   .8725722   .4868362     1.79   0.074    -.0842963    1.829441
        L1event_b |   .7434445   .5607407     1.33   0.186     -.358682    1.845571
        L2event_b |      .6816   .5582143     1.22   0.223    -.4155608    1.778761
        L3event_b |   .0027275   .6059024     0.00   0.996    -1.188164    1.193619
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |          0  (omitted)
        F6event_c |          0  (omitted)
        F5event_c |    .167484   .4146618     0.40   0.686    -.6475269    .9824948
        F4event_c |  -.0907304     .25046    -0.36   0.717    -.5830054    .4015445
        F3event_c |  -.1475104   .3384663    -0.44   0.663    -.8127602    .5177394
        F2event_c |  -.5145346   .2548335    -2.02   0.044    -1.015406   -.0136636
        L0event_c |   2.742605   .3973832     6.90   0.000     1.961555    3.523655
        L1event_c |    2.55312   .4273398     5.97   0.000     1.713191     3.39305
        L2event_c |   2.673376   .4857638     5.50   0.000     1.718616    3.628137
        L3event_c |   1.624281   .3914041     4.15   0.000     .8549825    2.393579
        L4event_c |          0  (omitted)
        L5event_c |          0  (omitted)
        L6event_c |          0  (omitted)
        L7event_c |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   2.174794   1.327744     1.64   0.102    -.4348648    4.784453
         ew_biodt |   .4128746   .0301808    13.68   0.000     .3535547    .4721945
        ew_dtmihi |  -.1666574   .0665295    -2.51   0.013      -.29742   -.0358947
         ew_ledig |   .2840727   .0795841     3.57   0.000     .1276515    .4404939
       ew_married |   .3300289   .0791078     4.17   0.000     .1745439    .4855139
        wb_anteil |  -.2595179   .0241442   -10.75   0.000    -.3069729   -.2120628
          wb_ausl |  -.0698814   .0162266    -4.31   0.000    -.1017745   -.0379882
         wb_18t24 |  -.0354172   .0333094    -1.06   0.288    -.1008863    .0300518
         wb_25t34 |   .0394982   .0218578     1.81   0.071    -.0034629    .0824592
         wb_35t44 |  -.0224077   .0271421    -0.83   0.410    -.0757551    .0309396
         wb_45t59 |  -.0508996   .0221147    -2.30   0.022    -.0943656   -.0074336
          avg_dur |   .0295389   .0250855     1.18   0.240    -.0197663     .078844
          hh_kids |  -.0902585   .0513069    -1.76   0.079    -.1911014    .0105844
mpreis_flats_rent |  -.0145485   .0279887    -0.52   0.603    -.0695597    .0404628
            _cons |  -17.28103   11.83406    -1.46   0.145    -40.54069    5.978621
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03                 -0.05   
                                            (0.28)                (0.37)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22                  0.09   
                                            (0.28)                (0.37)                (0.40)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19                  0.04   
                                            (0.19)                (0.27)                (0.31)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49                  0.56   
                                            (0.40)                (0.45)                (0.50)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19                  0.48   
                                            (0.36)                (0.45)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62                 1.04*   
                                            (0.38)                (0.44)                (0.44)  
  (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42                  0.27   
                                            (0.28)                (0.47)                (0.48)  
  (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17                  0.29   
                                            (0.23)                (0.40)                (0.46)  
  (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07                  0.08   
                                            (0.20)                (0.32)                (0.37)  
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82                 -1.07*  
                                            (0.30)                (0.46)                (0.53)  
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**                -1.06*  
                                            (0.31)                (0.45)                (0.52)  
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84                 -0.40   
                                            (0.36)                (0.44)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21                 -0.00   
                                            (0.25)                (0.34)                (0.36)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20                 -0.20   
                                            (0.26)                (0.38)                (0.37)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23                  0.45   
                                            (0.18)                (0.30)                (0.32)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***              -3.80*** 
                                            (0.32)                (0.43)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***              -3.30*** 
                                            (0.34)                (0.47)                (0.47)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***              -2.68*** 
                                            (0.38)                (0.51)                (0.50)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.34                  -0.49                  -0.08  
                                          (0.24)                 (0.32)                 (0.28)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.23                   0.25                   0.10   
                                          (0.24)                 (0.29)                 (0.33)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.11                  -0.11                  -0.19  
                                          (0.20)                 (0.28)                 (0.29)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.70                  -0.23                  0.03   
                                          (0.36)                 (0.44)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.58                  0.10                   0.11   
                                          (0.35)                 (0.43)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    -0.26                  0.07                   0.10   
                                          (0.38)                 (0.48)                 (0.51)  
 (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.49                 -0.94*                  -0.52  
                                          (0.26)                 (0.41)                 (0.37)  
 (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.11                  -0.14  
                                          (0.26)                 (0.39)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.14                  -0.04                  0.08   
                                          (0.20)                 (0.32)                 (0.35)  
 (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.05                  0.14                   0.87   
                                          (0.30)                 (0.45)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t+1#)    0.48                   0.80                   0.74   
                                          (0.34)                 (0.53)                 (0.56)  
 (N0)x\hspace{.7cm}Reassignment (#t+2#)    0.28                   0.60                   0.68   
                                          (0.41)                 (0.58)                 (0.56)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    0.04                   0.29                   -0.09  
                                          (0.22)                 (0.35)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.13                  0.09                   -0.15  
                                          (0.21)                 (0.31)                 (0.34)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.01                  -0.28                 -0.51*  
                                          (0.18)                 (0.25)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   2.04***                1.75***                2.74*** 
                                          (0.30)                 (0.38)                 (0.40)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   2.51***                2.31***                2.55*** 
                                          (0.31)                 (0.42)                 (0.43)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   2.68***                2.73***                2.67*** 
                                          (0.38)                 (0.48)                 (0.49)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------

(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  56,    617) =      27.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9905
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4469
Number of clusters (sb_new)  =        618         Root MSE        =     1.6198

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.5415084   .5920516    -0.91   0.361    -1.704189    .6211722
        F6event_a |  -.1704121   .4828486    -0.35   0.724    -1.118638    .7778139
        F5event_a |  -.8866034   .5099263    -1.74   0.083    -1.888005    .1147981
        F4event_a |   -.407778   .2786784    -1.46   0.144    -.9550512    .1394952
        F3event_a |  -.0457794   .2546407    -0.18   0.857    -.5458468    .4542881
        F2event_a |  -.2841713   .2379362    -1.19   0.233    -.7514342    .1830917
        L0event_a |   .2032831   .2718166     0.75   0.455    -.3305147    .7370809
        L1event_a |   .3308999   .3289168     1.01   0.315    -.3150323     .976832
        L2event_a |   .5890898   .3387366     1.74   0.083    -.0761267    1.254306
        L3event_a |   .0605326   .3042658     0.20   0.842    -.5369895    .6580546
        L4event_a |   .0319908   1.171724     0.03   0.978    -2.269059     2.33304
        L5event_a |   2.913125   1.902015     1.53   0.126    -.8220827    6.648332
        L6event_a |   .3742022    1.40973     0.27   0.791    -2.394248    3.142653
        L7event_a |  -1.150806   .8703116    -1.32   0.187    -2.859938    .5583261
        F7event_b |  -.5530807   .5054899    -1.09   0.274     -1.54577    .4396086
        F6event_b |    .312921   .4617352     0.68   0.498    -.5938422    1.219684
        F5event_b |  -.6187988   .4484084    -1.38   0.168     -1.49939    .2617928
        F4event_b |  -.2738815   .2564546    -1.07   0.286    -.7775112    .2297482
        F3event_b |   .0119653   .2451177     0.05   0.961    -.4694007    .4933314
        F2event_b |  -.1281813   .2200425    -0.58   0.560    -.5603043    .3039417
        L0event_b |  -.4526922   .2443943    -1.85   0.064    -.9326378    .0272533
        L1event_b |  -.2989983     .34056    -0.88   0.380    -.9677955    .3697989
        L2event_b |  -.1034099   .3688574    -0.28   0.779    -.8277781    .6209582
        L3event_b |   .0511257   .4009592     0.13   0.899    -.7362846    .8385359
        L4event_b |   1.274759    .540995     2.36   0.019     .2123439    2.337174
        L5event_b |   .8935358   .5127954     1.74   0.082    -.1135001    1.900572
        L6event_b |   .7463972   .5250032     1.42   0.156    -.2846127    1.777407
        L7event_b |   2.353006   .4926352     4.78   0.000     1.385561    3.320451
        F7event_c |    .696229   .3385615     2.06   0.040     .0313564    1.361102
        F6event_c |   .4903461   .3648417     1.34   0.179    -.2261359    1.206828
        F5event_c |   .4074311   .2842683     1.43   0.152    -.1508196    .9656817
        F4event_c |  -.0312713   .2402185    -0.13   0.896    -.5030163    .4404737
        F3event_c |  -.0721563   .2328961    -0.31   0.757    -.5295214    .3852089
        F2event_c |   .1799421   .1910984     0.94   0.347      -.19534    .5552243
        L0event_c |  -.7643874   .2627907    -2.91   0.004     -1.28046   -.2483147
        L1event_c |  -.0200455   .2970803    -0.07   0.946    -.6034566    .5633656
        L2event_c |   .3533143   .3479829     1.02   0.310    -.3300601    1.036689
        L3event_c |   .3010104    .385112     0.78   0.435    -.4552788      1.0573
        L4event_c |   .9099478   1.132033     0.80   0.422    -1.313157    3.133052
        L5event_c |   2.748988   1.810061     1.52   0.129    -.8056398    6.303616
        L6event_c |   1.830269   .7955074     2.30   0.022     .2680386    3.392499
        L7event_c |   2.245402   .7863268     2.86   0.004     .7012006    3.789603
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   1.667499   1.082865     1.54   0.124    -.4590486    3.794047
         ew_biodt |   .7633003    .031698    24.08   0.000     .7010512    .8255494
        ew_dtmihi |  -.1677692   .0518764    -3.23   0.001     -.269645   -.0658935
         ew_ledig |   .4186241   .0715848     5.85   0.000     .2780447    .5592035
       ew_married |   .6301423   .0699016     9.01   0.000     .4928685    .7674161
        wb_anteil |  -.5319489   .0241183   -22.06   0.000    -.5793128    -.484585
          wb_ausl |  -.0510329   .0175824    -2.90   0.004    -.0855614   -.0165044
         wb_18t24 |  -.0418968   .0261812    -1.60   0.110     -.093312    .0095183
         wb_25t34 |  -.0178464   .0167698    -1.06   0.288    -.0507792    .0150865
         wb_35t44 |  -.0004493   .0210376    -0.02   0.983    -.0417632    .0408647
         wb_45t59 |  -.0230484   .0196015    -1.18   0.240    -.0615422    .0154454
          avg_dur |   .0179192   .0221914     0.81   0.420    -.0256607     .061499
          hh_kids |  -.1140515   .0359698    -3.17   0.002    -.1846896   -.0434134
mpreis_flats_rent |   .0119445   .0235674     0.51   0.612    -.0343376    .0582266
            _cons |   .0696459   10.08968     0.01   0.994    -19.74463    19.88392
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03                 -0.05   
                                            (0.28)                (0.37)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22                  0.09   
                                            (0.28)                (0.37)                (0.40)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19                  0.04   
                                            (0.19)                (0.27)                (0.31)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49                  0.56   
                                            (0.40)                (0.45)                (0.50)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19                  0.48   
                                            (0.36)                (0.45)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62                 1.04*   
                                            (0.38)                (0.44)                (0.44)  
  (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42                  0.27   
                                            (0.28)                (0.47)                (0.48)  
  (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17                  0.29   
                                            (0.23)                (0.40)                (0.46)  
  (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07                  0.08   
                                            (0.20)                (0.32)                (0.37)  
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82                 -1.07*  
                                            (0.30)                (0.46)                (0.53)  
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**                -1.06*  
                                            (0.31)                (0.45)                (0.52)  
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84                 -0.40   
                                            (0.36)                (0.44)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21                 -0.00   
                                            (0.25)                (0.34)                (0.36)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20                 -0.20   
                                            (0.26)                (0.38)                (0.37)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23                  0.45   
                                            (0.18)                (0.30)                (0.32)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***              -3.80*** 
                                            (0.32)                (0.43)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***              -3.30*** 
                                            (0.34)                (0.47)                (0.47)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***              -2.68*** 
                                            (0.38)                (0.51)                (0.50)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.34                  -0.49                  -0.08  
                                          (0.24)                 (0.32)                 (0.28)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.23                   0.25                   0.10   
                                          (0.24)                 (0.29)                 (0.33)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.11                  -0.11                  -0.19  
                                          (0.20)                 (0.28)                 (0.29)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.70                  -0.23                  0.03   
                                          (0.36)                 (0.44)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.58                  0.10                   0.11   
                                          (0.35)                 (0.43)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    -0.26                  0.07                   0.10   
                                          (0.38)                 (0.48)                 (0.51)  
 (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.49                 -0.94*                  -0.52  
                                          (0.26)                 (0.41)                 (0.37)  
 (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.11                  -0.14  
                                          (0.26)                 (0.39)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.14                  -0.04                  0.08   
                                          (0.20)                 (0.32)                 (0.35)  
 (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.05                  0.14                   0.87   
                                          (0.30)                 (0.45)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t+1#)    0.48                   0.80                   0.74   
                                          (0.34)                 (0.53)                 (0.56)  
 (N0)x\hspace{.7cm}Reassignment (#t+2#)    0.28                   0.60                   0.68   
                                          (0.41)                 (0.58)                 (0.56)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    0.04                   0.29                   -0.09  
                                          (0.22)                 (0.35)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.13                  0.09                   -0.15  
                                          (0.21)                 (0.31)                 (0.34)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.01                  -0.28                 -0.51*  
                                          (0.18)                 (0.25)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   2.04***                1.75***                2.74*** 
                                          (0.30)                 (0.38)                 (0.40)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   2.51***                2.31***                2.55*** 
                                          (0.31)                 (0.42)                 (0.43)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   2.68***                2.73***                2.67*** 
                                          (0.38)                 (0.48)                 (0.49)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------


                       ---------------------------------------------------
                                                                         
                       ---------------------------------------------------
                        (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.41  
                                                                 (0.28)  
                        (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.05  
                                                                 (0.25)  
                        (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.28  
                                                                 (0.24)  
                        (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.20   
                                                                 (0.27)  
                        (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.33   
                                                                 (0.33)  
                        (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.59   
                                                                 (0.34)  
                        (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.27  
                                                                 (0.26)  
                        (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01   
                                                                 (0.25)  
                        (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.13  
                                                                 (0.22)  
                        (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.45  
                                                                 (0.24)  
                        (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.30  
                                                                 (0.34)  
                        (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.10  
                                                                 (0.37)  
                        (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.03  
                                                                 (0.24)  
                        (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07  
                                                                 (0.23)  
                        (N+)x\hspace{.7cm}Reassignment (#t-2#)    0.18   
                                                                 (0.19)  
                        (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.76** 
                                                                 (0.26)  
                        (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.02  
                                                                 (0.30)  
                        (N+)x\hspace{.7cm}Reassignment (#t+2#)    0.35   
                                                                 (0.35)  
                        R2                                        0.99   
                        N                                         4,666  
                        Clean sample                                     
                        Balanced sample                                  
                       ---------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =      2,040
Absorbing 2 HDFE groups                           F(  56,    254) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.9907
                                                  Adj R-squared   =     0.9876
                                                  Within R-sq.    =     0.4511
Number of clusters (sb_new)  =        255         Root MSE        =     1.6196

                                    (Std. err. adjusted for 255 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.8664156   .6931146    -1.25   0.212    -2.231399    .4985679
        F6event_a |  -.4064743   .6043474    -0.67   0.502    -1.596644    .7836958
        F5event_a |  -1.030361   .6795664    -1.52   0.131    -2.368664    .3079413
        F4event_a |  -.5216032   .3946017    -1.32   0.187    -1.298711    .2555046
        F3event_a |    .031704   .3234656     0.10   0.922    -.6053121    .6687202
        F2event_a |  -.3034608   .3498878    -0.87   0.387    -.9925115    .3855899
        L0event_a |   .2534412   .3626016     0.70   0.485    -.4606474    .9675299
        L1event_a |   .2955713   .4539485     0.65   0.516    -.5984109    1.189554
        L2event_a |   .6832756   .4040496     1.69   0.092    -.1124385     1.47899
        L3event_a |   .0991232   .3834281     0.26   0.796    -.6559799    .8542264
        L4event_a |  -1.023674   1.902971    -0.54   0.591    -4.771285    2.723937
        L5event_a |   2.811416   2.014686     1.40   0.164    -1.156201    6.779034
        L6event_a |    .456836   1.632223     0.28   0.780    -2.757578     3.67125
        L7event_a |  -.5116408   .9075747    -0.56   0.573    -2.298971    1.275689
        F7event_b |  -.7904818   .6454093    -1.22   0.222    -2.061517    .4805535
        F6event_b |   .0366434   .5726447     0.06   0.949    -1.091093     1.16438
        F5event_b |  -.8434021   .7121474    -1.18   0.237    -2.245868    .5590637
        F4event_b |  -.5198612   .4616702    -1.13   0.261     -1.42905    .3893279
        F3event_b |  -.2831395   .3845975    -0.74   0.462    -1.040546    .4742666
        F2event_b |  -.1111642   .3611999    -0.31   0.759    -.8224924    .6001639
        L0event_b |  -.6797266   .3632473    -1.87   0.062    -1.395087    .0356336
        L1event_b |  -.4287176   .4355673    -0.98   0.326    -1.286501    .4290658
        L2event_b |  -.2402216   .4475432    -0.54   0.592     -1.12159    .6411466
        L3event_b |   .1769128   .5418109     0.33   0.744    -.8901012    1.243927
        L4event_b |   .5675152   .7284514     0.78   0.437    -.8670587    2.002089
        L5event_b |   .5978725   .6616003     0.90   0.367    -.7050484    1.900793
        L6event_b |     .78512   .7102116     1.11   0.270    -.6135334    2.183773
        L7event_b |   2.131006   .7380385     2.89   0.004      .677552    3.584461
        F7event_c |   .4327611   .4831552     0.90   0.371    -.5187393    1.384262
        F6event_c |   .6246967    .477947     1.31   0.192    -.3165471    1.565941
        F5event_c |   .0679341   .4992038     0.14   0.892    -.9151717     1.05104
        F4event_c |   .0852714   .3252335     0.26   0.793    -.5552263    .7257692
        F3event_c |  -.1121714   .3364295    -0.33   0.739     -.774718    .5503753
        F2event_c |  -.0556352   .2825621    -0.20   0.844    -.6120983    .5008278
        L0event_c |  -1.358986   .3947162    -3.44   0.001    -2.136319   -.5816522
        L1event_c |  -.6255966   .4422061    -1.41   0.158    -1.496454    .2452609
        L2event_c |   .0165071   .4562618     0.04   0.971     -.882031    .9150451
        L3event_c |   .1122586   .5214696     0.22   0.830    -.9146962    1.139214
        L4event_c |    .152132   .5665702     0.27   0.789    -.9636415    1.267906
        L5event_c |   .3406902    .926242     0.37   0.713    -1.483402    2.164783
        L6event_c |   .8467552   1.295521     0.65   0.514    -1.704575    3.398086
        L7event_c |   2.073589   .7343459     2.82   0.005     .6274066    3.519771
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   1.939237   1.892757     1.02   0.307    -1.788259    5.666732
         ew_biodt |   .7314918   .0525279    13.93   0.000      .628046    .8349376
        ew_dtmihi |  -.1181114   .0798213    -1.48   0.140    -.2753072    .0390845
         ew_ledig |   .4734971   .0931582     5.08   0.000     .2900363    .6569579
       ew_married |   .7450811   .0953487     7.81   0.000     .5573064    .9328559
        wb_anteil |  -.5138416   .0348741   -14.73   0.000    -.5825208   -.4451625
          wb_ausl |  -.0534494   .0227635    -2.35   0.020    -.0982785   -.0086202
         wb_18t24 |  -.0583235   .0384784    -1.52   0.131    -.1341009    .0174539
         wb_25t34 |  -.0468585   .0285795    -1.64   0.102    -.1031415    .0094244
         wb_35t44 |  -.0241917   .0292267    -0.83   0.409    -.0817492    .0333658
         wb_45t59 |   -.015019   .0290081    -0.52   0.605     -.072146    .0421081
          avg_dur |  -.0157539   .0374597    -0.42   0.674    -.0895251    .0580173
          hh_kids |  -.1374861   .0548299    -2.51   0.013    -.2454653    -.029507
mpreis_flats_rent |   -.021073     .03834    -0.55   0.583    -.0965778    .0544319
            _cons |  -5.896692   16.56161    -0.36   0.722    -38.51225    26.71887
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       255         255           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03                 -0.05   
                                            (0.28)                (0.37)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22                  0.09   
                                            (0.28)                (0.37)                (0.40)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19                  0.04   
                                            (0.19)                (0.27)                (0.31)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49                  0.56   
                                            (0.40)                (0.45)                (0.50)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19                  0.48   
                                            (0.36)                (0.45)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62                 1.04*   
                                            (0.38)                (0.44)                (0.44)  
  (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42                  0.27   
                                            (0.28)                (0.47)                (0.48)  
  (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17                  0.29   
                                            (0.23)                (0.40)                (0.46)  
  (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07                  0.08   
                                            (0.20)                (0.32)                (0.37)  
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82                 -1.07*  
                                            (0.30)                (0.46)                (0.53)  
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**                -1.06*  
                                            (0.31)                (0.45)                (0.52)  
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84                 -0.40   
                                            (0.36)                (0.44)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21                 -0.00   
                                            (0.25)                (0.34)                (0.36)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20                 -0.20   
                                            (0.26)                (0.38)                (0.37)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23                  0.45   
                                            (0.18)                (0.30)                (0.32)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***              -3.80*** 
                                            (0.32)                (0.43)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***              -3.30*** 
                                            (0.34)                (0.47)                (0.47)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***              -2.68*** 
                                            (0.38)                (0.51)                (0.50)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.34                  -0.49                  -0.08  
                                          (0.24)                 (0.32)                 (0.28)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.23                   0.25                   0.10   
                                          (0.24)                 (0.29)                 (0.33)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.11                  -0.11                  -0.19  
                                          (0.20)                 (0.28)                 (0.29)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.70                  -0.23                  0.03   
                                          (0.36)                 (0.44)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.58                  0.10                   0.11   
                                          (0.35)                 (0.43)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    -0.26                  0.07                   0.10   
                                          (0.38)                 (0.48)                 (0.51)  
 (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.49                 -0.94*                  -0.52  
                                          (0.26)                 (0.41)                 (0.37)  
 (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.11                  -0.14  
                                          (0.26)                 (0.39)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.14                  -0.04                  0.08   
                                          (0.20)                 (0.32)                 (0.35)  
 (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.05                  0.14                   0.87   
                                          (0.30)                 (0.45)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t+1#)    0.48                   0.80                   0.74   
                                          (0.34)                 (0.53)                 (0.56)  
 (N0)x\hspace{.7cm}Reassignment (#t+2#)    0.28                   0.60                   0.68   
                                          (0.41)                 (0.58)                 (0.56)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    0.04                   0.29                   -0.09  
                                          (0.22)                 (0.35)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.13                  0.09                   -0.15  
                                          (0.21)                 (0.31)                 (0.34)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.01                  -0.28                 -0.51*  
                                          (0.18)                 (0.25)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   2.04***                1.75***                2.74*** 
                                          (0.30)                 (0.38)                 (0.40)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   2.51***                2.31***                2.55*** 
                                          (0.31)                 (0.42)                 (0.43)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   2.68***                2.73***                2.67*** 
                                          (0.38)                 (0.48)                 (0.49)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------


     ----------------------------------------------------------------------------------------
                                                        \multicolumn{3}{c}{turnout_tot_req} 
     ----------------------------------------------------------------------------------------
      (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.41                  -0.52                
                                               (0.28)                 (0.39)                
      (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.05                  0.03                 
                                               (0.25)                 (0.32)                
      (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.28                  -0.30                
                                               (0.24)                 (0.35)                
      (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.20                   0.25                 
                                               (0.27)                 (0.36)                
      (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.33                   0.30                 
                                               (0.33)                 (0.45)                
      (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.59                   0.68                 
                                               (0.34)                 (0.40)                
      (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.52                
                                               (0.26)                 (0.46)                
      (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.28                
                                               (0.25)                 (0.38)                
      (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.13                  -0.11                
                                               (0.22)                 (0.36)                
      (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.45                  -0.68                
                                               (0.24)                 (0.36)                
      (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.30                  -0.43                
                                               (0.34)                 (0.44)                
      (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.10                  -0.24                
                                               (0.37)                 (0.45)                
      (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.03                  0.09                 
                                               (0.24)                 (0.33)                
      (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  -0.11                
                                               (0.23)                 (0.34)                
      (N+)x\hspace{.7cm}Reassignment (#t-2#)    0.18                   -0.06                
                                               (0.19)                 (0.28)                
      (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.76**               -1.36***               
                                               (0.26)                 (0.39)                
      (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.02                  -0.63                
                                               (0.30)                 (0.44)                
      (N+)x\hspace{.7cm}Reassignment (#t+2#)    0.35                   0.02                 
                                               (0.35)                 (0.46)                
      R2                                        0.99                   0.99                 
      N                                         4,666                  2,040                
      Clean sample                                                       X                  
      Balanced sample                                                                       
     ----------------------------------------------------------------------------------------

(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F7event_c omitted because of collinearity
note: F6event_c omitted because of collinearity
note: L4event_c omitted because of collinearity
note: L5event_c omitted because of collinearity
note: L6event_c omitted because of collinearity
note: L7event_c omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  38,    431) =      32.67
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9879
                                                  Within R-sq.    =     0.4612
Number of clusters (sb_new)  =        432         Root MSE        =     1.6355

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |   .4731224   .8526701     0.55   0.579    -1.202786    2.149031
        F4event_a |   -.138396   .3512581    -0.39   0.694    -.8287879    .5519958
        F3event_a |   .1907535   .3826793     0.50   0.618    -.5613963    .9429034
        F2event_a |  -.1496427   .3766901    -0.40   0.691    -.8900208    .5907354
        L0event_a |   .5918446   .3622059     1.63   0.103    -.1200651    1.303754
        L1event_a |   .5909844   .4500481     1.31   0.190    -.2935776    1.475546
        L2event_a |   1.137788    .374032     3.04   0.002     .4026347    1.872942
        L3event_a |   .0496226    .357885     0.14   0.890    -.6537943    .7530395
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |   .2513122   .9176818     0.27   0.784    -1.552376       2.055
        F4event_b |  -.2482019   .5554887    -0.45   0.655    -1.340006    .8436018
        F3event_b |   .1532137   .5210858     0.29   0.769    -.8709718    1.177399
        F2event_b |   .1642809    .499099     0.33   0.742    -.8166899    1.145252
        L0event_b |  -.1952086   .4453701    -0.44   0.661    -1.070576     .680159
        L1event_b |  -.3198805   .5037771    -0.63   0.526    -1.310046    .6702851
        L2event_b |   .2848119   .4569381     0.62   0.533    -.6132923    1.182916
        L3event_b |    .143927   .6023823     0.24   0.811    -1.040045    1.327899
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |          0  (omitted)
        F6event_c |          0  (omitted)
        F5event_c |   .3185478   .5524594     0.58   0.565     -.767302    1.404398
        F4event_c |  -.0938948   .2973813    -0.32   0.752    -.6783927    .4906031
        F3event_c |  -.3441979   .3491473    -0.99   0.325    -1.030441    .3420454
        F2event_c |  -.0621199   .3132568    -0.20   0.843    -.6778209    .5535812
        L0event_c |  -1.053142   .4602268    -2.29   0.023     -1.95771    -.148574
        L1event_c |  -.7468175    .447229    -1.67   0.096    -1.625839    .1322036
        L2event_c |  -.0077473   .4454861    -0.02   0.986    -.8833428    .8678482
        L3event_c |    .027064   .4645747     0.06   0.954    -.8860499    .9401779
        L4event_c |          0  (omitted)
        L5event_c |          0  (omitted)
        L6event_c |          0  (omitted)
        L7event_c |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   1.104571   1.081386     1.02   0.308    -1.020875    3.230017
         ew_biodt |   .7757222   .0348231    22.28   0.000      .707278    .8441664
        ew_dtmihi |  -.1627279   .0565672    -2.88   0.004    -.2739097    -.051546
         ew_ledig |   .4937766   .0714432     6.91   0.000     .3533562     .634197
       ew_married |   .6871377   .0710963     9.66   0.000     .5473992    .8268762
        wb_anteil |   -.530898   .0262068   -20.26   0.000     -.582407   -.4793889
          wb_ausl |  -.0408186   .0213886    -1.91   0.057    -.0828575    .0012202
         wb_18t24 |  -.0444003   .0301785    -1.47   0.142    -.1037156     .014915
         wb_25t34 |  -.0137129   .0200021    -0.69   0.493    -.0530266    .0256009
         wb_35t44 |  -.0216406   .0244929    -0.88   0.377    -.0697809    .0264998
         wb_45t59 |  -.0180354   .0221366    -0.81   0.416    -.0615444    .0254737
          avg_dur |   .0032727   .0253458     0.13   0.897     -.046544    .0530894
          hh_kids |   -.092808   .0423772    -2.19   0.029    -.1760996   -.0095163
mpreis_flats_rent |   .0255888   .0279814     0.91   0.361    -.0294081    .0805857
            _cons |  -2.626643   10.61409    -0.25   0.805    -23.48846    18.23517
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03                 -0.05   
                                            (0.28)                (0.37)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22                  0.09   
                                            (0.28)                (0.37)                (0.40)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19                  0.04   
                                            (0.19)                (0.27)                (0.31)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49                  0.56   
                                            (0.40)                (0.45)                (0.50)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19                  0.48   
                                            (0.36)                (0.45)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62                 1.04*   
                                            (0.38)                (0.44)                (0.44)  
  (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42                  0.27   
                                            (0.28)                (0.47)                (0.48)  
  (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17                  0.29   
                                            (0.23)                (0.40)                (0.46)  
  (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07                  0.08   
                                            (0.20)                (0.32)                (0.37)  
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82                 -1.07*  
                                            (0.30)                (0.46)                (0.53)  
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**                -1.06*  
                                            (0.31)                (0.45)                (0.52)  
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84                 -0.40   
                                            (0.36)                (0.44)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21                 -0.00   
                                            (0.25)                (0.34)                (0.36)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20                 -0.20   
                                            (0.26)                (0.38)                (0.37)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23                  0.45   
                                            (0.18)                (0.30)                (0.32)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***              -3.80*** 
                                            (0.32)                (0.43)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***              -3.30*** 
                                            (0.34)                (0.47)                (0.47)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***              -2.68*** 
                                            (0.38)                (0.51)                (0.50)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.34                  -0.49                  -0.08  
                                          (0.24)                 (0.32)                 (0.28)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.23                   0.25                   0.10   
                                          (0.24)                 (0.29)                 (0.33)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.11                  -0.11                  -0.19  
                                          (0.20)                 (0.28)                 (0.29)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.70                  -0.23                  0.03   
                                          (0.36)                 (0.44)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.58                  0.10                   0.11   
                                          (0.35)                 (0.43)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    -0.26                  0.07                   0.10   
                                          (0.38)                 (0.48)                 (0.51)  
 (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.49                 -0.94*                  -0.52  
                                          (0.26)                 (0.41)                 (0.37)  
 (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.11                  -0.14  
                                          (0.26)                 (0.39)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.14                  -0.04                  0.08   
                                          (0.20)                 (0.32)                 (0.35)  
 (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.05                  0.14                   0.87   
                                          (0.30)                 (0.45)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t+1#)    0.48                   0.80                   0.74   
                                          (0.34)                 (0.53)                 (0.56)  
 (N0)x\hspace{.7cm}Reassignment (#t+2#)    0.28                   0.60                   0.68   
                                          (0.41)                 (0.58)                 (0.56)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    0.04                   0.29                   -0.09  
                                          (0.22)                 (0.35)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.13                  0.09                   -0.15  
                                          (0.21)                 (0.31)                 (0.34)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.01                  -0.28                 -0.51*  
                                          (0.18)                 (0.25)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   2.04***                1.75***                2.74*** 
                                          (0.30)                 (0.38)                 (0.40)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   2.51***                2.31***                2.55*** 
                                          (0.31)                 (0.42)                 (0.43)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   2.68***                2.73***                2.67*** 
                                          (0.38)                 (0.48)                 (0.49)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------


 ------------------------------------------------------------------------------------------------
                                                    \multicolumn{3}{c}{turnout_tot_req}         
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.41                  -0.52                 -0.14  
                                           (0.28)                 (0.39)                 (0.35) 
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.05                  0.03                   0.19  
                                           (0.25)                 (0.32)                 (0.38) 
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.28                  -0.30                 -0.15  
                                           (0.24)                 (0.35)                 (0.38) 
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.20                   0.25                   0.59  
                                           (0.27)                 (0.36)                 (0.36) 
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.33                   0.30                   0.59  
                                           (0.33)                 (0.45)                 (0.45) 
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.59                   0.68                  1.14** 
                                           (0.34)                 (0.40)                 (0.37) 
  (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.52                 -0.25  
                                           (0.26)                 (0.46)                 (0.56) 
  (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.28                  0.15  
                                           (0.25)                 (0.38)                 (0.52) 
  (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.13                  -0.11                  0.16  
                                           (0.22)                 (0.36)                 (0.50) 
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.45                  -0.68                 -0.20  
                                           (0.24)                 (0.36)                 (0.45) 
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.30                  -0.43                 -0.32  
                                           (0.34)                 (0.44)                 (0.50) 
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.10                  -0.24                  0.28  
                                           (0.37)                 (0.45)                 (0.46) 
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.03                  0.09                  -0.09  
                                           (0.24)                 (0.33)                 (0.30) 
  (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  -0.11                 -0.34  
                                           (0.23)                 (0.34)                 (0.35) 
  (N+)x\hspace{.7cm}Reassignment (#t-2#)    0.18                   -0.06                 -0.06  
                                           (0.19)                 (0.28)                 (0.31) 
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.76**               -1.36***                -1.05* 
                                           (0.26)                 (0.39)                 (0.46) 
  (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.02                  -0.63                 -0.75  
                                           (0.30)                 (0.44)                 (0.45) 
  (N+)x\hspace{.7cm}Reassignment (#t+2#)    0.35                   0.02                  -0.01  
                                           (0.35)                 (0.46)                 (0.45) 
  R2                                        0.99                   0.99                   0.99  
  N                                         4,666                  2,040                 3,456  
  Clean sample                                                       X                          
  Balanced sample                                                                          X    
 ------------------------------------------------------------------------------------------------


.         outreg using "$tables/Table_C4_ES_het_by_distance_3clust", replay tex replace fragment
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_C4
> _ES_het_by_distance_3clust.tex not found)
 ------------------------------------------------------------------------------------------------
                                                     \multicolumn{3}{c}{turnout_urne}           
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.06                 -0.03                 -0.05   
                                            (0.28)                (0.37)                (0.36)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.28                 -0.22                  0.09   
                                            (0.28)                (0.37)                (0.40)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.17                 -0.19                  0.04   
                                            (0.19)                (0.27)                (0.31)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.90*                  0.49                  0.56   
                                            (0.40)                (0.45)                (0.50)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.92*                  0.19                  0.48   
                                            (0.36)                (0.45)                (0.46)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.85*                  0.62                 1.04*   
                                            (0.38)                (0.44)                (0.44)  
  (N0)x\hspace{.7cm}Reassignment (#t-4#)     0.21                  0.42                  0.27   
                                            (0.28)                (0.47)                (0.48)  
  (N0)x\hspace{.7cm}Reassignment (#t-3#)     0.00                 -0.17                  0.29   
                                            (0.23)                (0.40)                (0.46)  
  (N0)x\hspace{.7cm}Reassignment (#t-2#)     0.01                 -0.07                  0.08   
                                            (0.20)                (0.32)                (0.37)  
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.40                 -0.82                 -1.07*  
                                            (0.30)                (0.46)                (0.53)  
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.78*               -1.23**                -1.06*  
                                            (0.31)                (0.45)                (0.52)  
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.38                 -0.84                 -0.40   
                                            (0.36)                (0.44)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.07                 -0.21                 -0.00   
                                            (0.25)                (0.34)                (0.36)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.06                 -0.20                 -0.20   
                                            (0.26)                (0.38)                (0.37)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.19                  0.23                  0.45   
                                            (0.18)                (0.30)                (0.32)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.81***              -3.11***              -3.80*** 
                                            (0.32)                (0.43)                (0.44)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.53***              -2.94***              -3.30*** 
                                            (0.34)                (0.47)                (0.47)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -2.32***              -2.71***              -2.68*** 
                                            (0.38)                (0.51)                (0.50)  
  R2                                         0.97                  0.98                  0.98   
  N                                         4,666                 2,040                 3,456   
  Clean sample                                                      X                           
  Balanced sample                                                                         X     
 ------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------
                                                   \multicolumn{3}{c}{turnout_pos_req}          
-------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.34                  -0.49                  -0.08  
                                          (0.24)                 (0.32)                 (0.28)  
 (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.23                   0.25                   0.10   
                                          (0.24)                 (0.29)                 (0.33)  
 (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.11                  -0.11                  -0.19  
                                          (0.20)                 (0.28)                 (0.29)  
 (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.70                  -0.23                  0.03   
                                          (0.36)                 (0.44)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.58                  0.10                   0.11   
                                          (0.35)                 (0.43)                 (0.47)  
 (N-)x\hspace{.7cm}Reassignment (#t+2#)    -0.26                  0.07                   0.10   
                                          (0.38)                 (0.48)                 (0.51)  
 (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.49                 -0.94*                  -0.52  
                                          (0.26)                 (0.41)                 (0.37)  
 (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.11                  -0.14  
                                          (0.26)                 (0.39)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.14                  -0.04                  0.08   
                                          (0.20)                 (0.32)                 (0.35)  
 (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.05                  0.14                   0.87   
                                          (0.30)                 (0.45)                 (0.49)  
 (N0)x\hspace{.7cm}Reassignment (#t+1#)    0.48                   0.80                   0.74   
                                          (0.34)                 (0.53)                 (0.56)  
 (N0)x\hspace{.7cm}Reassignment (#t+2#)    0.28                   0.60                   0.68   
                                          (0.41)                 (0.58)                 (0.56)  
 (N+)x\hspace{.7cm}Reassignment (#t-4#)    0.04                   0.29                   -0.09  
                                          (0.22)                 (0.35)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.13                  0.09                   -0.15  
                                          (0.21)                 (0.31)                 (0.34)  
 (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.01                  -0.28                 -0.51*  
                                          (0.18)                 (0.25)                 (0.25)  
 (N+)x\hspace{.7cm}Reassignment (#t+0#)   2.04***                1.75***                2.74*** 
                                          (0.30)                 (0.38)                 (0.40)  
 (N+)x\hspace{.7cm}Reassignment (#t+1#)   2.51***                2.31***                2.55*** 
                                          (0.31)                 (0.42)                 (0.43)  
 (N+)x\hspace{.7cm}Reassignment (#t+2#)   2.68***                2.73***                2.67*** 
                                          (0.38)                 (0.48)                 (0.49)  
 R2                                        0.96                   0.97                   0.96   
 N                                         4,666                  2,040                  3,456  
 Clean sample                                                       X                           
 Balanced sample                                                                           X    
-------------------------------------------------------------------------------------------------


 ------------------------------------------------------------------------------------------------
                                                    \multicolumn{3}{c}{turnout_tot_req}         
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.41                  -0.52                 -0.14  
                                           (0.28)                 (0.39)                 (0.35) 
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.05                  0.03                   0.19  
                                           (0.25)                 (0.32)                 (0.38) 
  (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.28                  -0.30                 -0.15  
                                           (0.24)                 (0.35)                 (0.38) 
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    0.20                   0.25                   0.59  
                                           (0.27)                 (0.36)                 (0.36) 
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    0.33                   0.30                   0.59  
                                           (0.33)                 (0.45)                 (0.45) 
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    0.59                   0.68                  1.14** 
                                           (0.34)                 (0.40)                 (0.37) 
  (N0)x\hspace{.7cm}Reassignment (#t-4#)    -0.27                  -0.52                 -0.25  
                                           (0.26)                 (0.46)                 (0.56) 
  (N0)x\hspace{.7cm}Reassignment (#t-3#)    0.01                   -0.28                  0.15  
                                           (0.25)                 (0.38)                 (0.52) 
  (N0)x\hspace{.7cm}Reassignment (#t-2#)    -0.13                  -0.11                  0.16  
                                           (0.22)                 (0.36)                 (0.50) 
  (N0)x\hspace{.7cm}Reassignment (#t+0#)    -0.45                  -0.68                 -0.20  
                                           (0.24)                 (0.36)                 (0.45) 
  (N0)x\hspace{.7cm}Reassignment (#t+1#)    -0.30                  -0.43                 -0.32  
                                           (0.34)                 (0.44)                 (0.50) 
  (N0)x\hspace{.7cm}Reassignment (#t+2#)    -0.10                  -0.24                  0.28  
                                           (0.37)                 (0.45)                 (0.46) 
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.03                  0.09                  -0.09  
                                           (0.24)                 (0.33)                 (0.30) 
  (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.07                  -0.11                 -0.34  
                                           (0.23)                 (0.34)                 (0.35) 
  (N+)x\hspace{.7cm}Reassignment (#t-2#)    0.18                   -0.06                 -0.06  
                                           (0.19)                 (0.28)                 (0.31) 
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.76**               -1.36***                -1.05* 
                                           (0.26)                 (0.39)                 (0.46) 
  (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.02                  -0.63                 -0.75  
                                           (0.30)                 (0.44)                 (0.45) 
  (N+)x\hspace{.7cm}Reassignment (#t+2#)    0.35                   0.02                  -0.01  
                                           (0.35)                 (0.46)                 (0.45) 
  R2                                        0.99                   0.99                   0.99  
  N                                         4,666                  2,040                 3,456  
  Clean sample                                                       X                          
  Balanced sample                                                                          X    
 ------------------------------------------------------------------------------------------------


.         cleantex "$tables/Table_C4_ES_het_by_distance_3clust.tex" , nodisplay   replace

. 
end of do-file
Running: 03c_party_outcomes_figures_11_d14.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
>         
> Output: Figure 11, D.14
> 
> Task: Explore partisan effects of reassignments
> 
> 
> */      
.         
. * PULL: precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
. ********************************************************************************
. *        Prep Estimation *
. ********************************************************************************
.         
. * PREP party outcomes
.         
.         // classify 6 largest parties in Left-Right
.         gen shr_left    = shr_dielinke + shr_spd + shr_gruene 

.         lab var shr_left        "Party share LEFT (links spd grue)"

.         gen shr_cons    = shr_fdp + shr_csu  + shr_freiewaehler 

.         lab var shr_cons  "Party share RIGHT (csu fw afd)"

.         gen anz_left    = anz_dielinke + anz_spd + shr_gruene 

.         lab var anz_left   "Nbr Votes LEFT (links spd grue)"

.         gen anz_cons    = shr_fdp + anz_csu  + anz_freiewaehler 

.         lab var anz_cons  "Nbr Votes RIGHT (csu fw afd)"

.         
.         // insert missings instead of zeros for party shares where party wasn't on the ballot
.         foreach v of varlist shr_* {
  2.                 forvalues j =1/8 {
  3.                         qui su `v' if wahl_id==`j'
  4.                         qui replace `v' =. if r(mean)==0 & wahl_id==`j'
  5.                 }
  6.                 assert `v' <1 if !missing(`v')
  7.                 qui replace `v'= `v'*100 // rescale 0-100
  8.         }

.         
.         // gen votes rel to eligible voter
.         * in KOW14 und KOW20 => #votes > #eligible voters!
.         foreach v of varlist anz_* {
  2.                 gen      rel_`v'  =  100* `v' /wahlber_gesamt if !inlist(wahl_id,3,8)
  3.                 replace  rel_`v' =  100* `v' /(wahlber_gesamt*80) if inlist(wahl_id,3,8)
  4.                 assert inrange(rel_`v',0,100)
  5.         }       
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)
(1,236 missing values generated)
(1,236 real changes made)

.         
. 
. ********************************************************************************
. *        Party outcomes at the polling place: ALL PARTIES (Figure D14)
. ********************************************************************************                
. 
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         order F1event, last

. 
. 
.         // Estimate baseline ES
.         estimates clear

.         global dpv dielinke spd gruene fdp csu freiewaehler

.         
.         foreach r in shr rel_anz {
  2.                 foreach v in $dpv {     
  3.                         qui reghdfe `r'_`v' F7event-L7event F1event $ctr $wgt if smpl_trim ==
> 1, absorb(i.wahl_id##i.stadtbez i.sb_new) cluster(sb_new)
  4.                         estimates store `v'
  5.                                         
.                         // store means
.                         qui su `r'_`v' $wgt
  6.                         local mean_`v':di %12.1f r(mean)
  7.                         local m`v'=subinstr("`mean_`v''"," ","",.)
  8.                 }               
  9.                         // Lables
.                         if "`r'"=="rel_anz" local title "{bf:Panel A.} Effect on Party Turnout"
 10.                         if "`r'"=="rel_anz" local ytitle "Party turnout in %" "(estimates)"  
>            
 11.                         if "`r'"=="shr"         local title "{bf:Panel B.} Effect on Party Vo
> te Shares"
 12.                         if "`r'"=="shr"         local ytitle "Party vote share in %" "(estima
> tes)" 
 13.                         if "`r'"=="rel_anz" local gap "-6"
 14.                         if "`r'"=="shr"         local gap "3"
 15.                         
.                         
.                         // PLOT: FIGURE D14. Effect of Reassignments on Party Vote (single parti
> es)
.                         event_plot  $dpv, ///
>                         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap
> ) ///
>                         together perturb(-0.25(0.1)0.25) trimlead(4) trimlag(2) noautolegend ///
>                         graph_opt(xtitle("") ytitle("`ytitle'", size(small)) ysc(titlegap(`gap')
> ) xlabel(-4(1)2) xtitle("Election since reassignment") ///
>                                 legend(pos(12) order(1 "LINKE (`mdielinke' %)" 3 "SPD (`mspd' %)
> " 5 "Grüne (`mgruene' %)" 7 "FDP (`mfdp' %)" ///
>                                 9 "CSU (`mcsu' %)" 11 "FW (`mfreiewaehler' %)" /*13 "AFD (`mafd'
> )"*/) size(small) row(1) region(style(none)) title("Outcomes (means):",size(medsmall) bexpand ju
> st(left)) ) ///
>                                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray)
>  lpat(solid)) ylabel(, angle(horizontal)) ///
>                                 title("`title'", nobox span bexpand justification(left) size(med
> ium)) name(`r', replace)) ///
>                         lag_opt1(msymbol(S) msize(small) color(purple)) lag_ci_opt1(color(purple
> )) ///
>                         lag_opt2(msymbol(O) msize(small) color(red)) lag_ci_opt2(color(red)) ///
>                         lag_opt3(msymbol(T) msize(small) color(green)) lag_ci_opt3(color(green))
>  ///
>                         lag_opt4(msymbol(Dh) msize(small) color(gold)) lag_ci_opt4(color(gold)) 
> ///
>                         lag_opt5(msymbol(Sh) msize(small) color(black)) lag_ci_opt5(color(black)
> ) ///
>                         lag_opt6(msymbol(Oh) msize(small) color(orange)) lag_ci_opt6(color(orang
> e))
 16.                 
.         }

.                 
.         graph combine  rel_anz shr , xcommon col(1) iscale(.8)

.         gr_edit .style.editstyle declared_ysize(4.7) editcopy

.         graph export "$figures/Figure_D14_ES_party_outc_all.pdf", replace                       
>         
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D14_ES
    > _party_outc_all.pdf saved as PDF format

.                 
. ********************************************************************************
. *        Party outcomes at the polling place LEFT-RIGHT (Figure 11)
. ********************************************************************************
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         order F1event, last

. 
.         // Estimate baseline ES
.         estimates clear

.         global dpv rel_anz_left shr_left rel_anz_cons  shr_cons

.         
.         
.         foreach v of varlist $dpv {     
  2.                   qui reghdfe `v' F7event-L7event F1event $ctr $wgt  if smpl_trim ==1, absorb
> (i.wahl_id##i.stadtbez i.sb_new) cluster(sb_new)
  3.                 
.                 estimates store `v'
  4.                 
.                 qui su `v' $wgt
  5.                 local mean_`v':di %12.1f r(mean)
  6.                 local m`v'=subinstr("`mean_`v''"," ","",.)
  7.         }

.                 
.                 
.         // PLOT: LEFT RIGHT PARTY TURNOUT
.         event_plot  rel_anz_left  rel_anz_cons , ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Party turnout in %""(estimates)", size(medsmall)) xlabel(-4
> (1)2) xtitle("Election since reassignment") ///
>                 legend(pos(12) order(1 "Left-wing (`mrel_anz_left' %)" 3 "Right-wing (`mrel_anz_
> cons' %)") row(1) region(style(none)) size(medsmall) title("Outcomes (means):",size(medsmall) be
> xpand just(left))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 name(turnout, replace)) ///
>         lag_opt1(msymbol(O) msize(2.5pt) color(maroon))         lag_ci_opt1(color(maroon)) ///
>         lag_opt2(msymbol(S) msize(2.5pt) color(black)) lag_ci_opt2(color(black))        

.         
.         
.         // PLOT: LEFT RIGHT PARTY VOTE SHARES
.         event_plot  shr_left shr_cons , ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Party vote share in %""(estimates)", size(medsmall)) xlabel
> (-4(1)2) xtitle("Election since reassignment") ///
>                 legend(pos(12) order(1 "Left-wing (`mshr_left' %)" 3  "Right-wing (`mshr_cons' %
> )"   ) row(1) region(style(none)) size(medsmall) title("Outcomes (means):",size(medsmall) bexpan
> d just(left))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 name(shares, replace) ) ///
>         lag_opt1(msymbol(O) msize(2.5pt) color(maroon))  lag_ci_opt1(color(maroon)) ///
>         lag_opt2(msymbol(S) msize(2.5pt) color(black)) lag_ci_opt2(color(black))        

.         
. 
.         
. * Test for equality of estimates (since different outcomes => need to duplicate data)
. frame change default

. frame copy default tmp, replace

. frame tmp{      
.         // duplicate dataset
.         gen id=_n
.         expand 2
(4,944 observations created)
.         
.         // gen dataset id
.         bys id: gen idset = _n-1
.         
.         // rename outcomes
.         gen     y_shr = shr_left         if idset==1
(4,944 missing values generated)
.         replace y_shr = shr_cons         if idset==0
(4,944 real changes made)
.         gen     y_rel = rel_anz_left if idset==1
(4,944 missing values generated)
.         replace y_rel = rel_anz_cons if idset==0        
(4,944 real changes made)
.         
.         // gen leads and lags
.         cap drop L* F*
.         forvalues l = 7(-1)1 {
  2.                 gen     F`l'event = K==-`l'
  3.                 gen     F`l'event_int =F`l'event*idset  // interact w/ dataset id
  4.         }       
.         forvalues l = 0/7 {
  2.                 gen     L`l'event = K==`l'
  3.                 gen     L`l'event_int =L`l'event*idset
  4.         }
.         order *_int, last
.         order F1event*, last
. 
.         // Estimate baseline ES
.         estimates clear
. 
.         global dpv y_rel y_shr 
.         
.         
.         foreach v of varlist $dpv {     
  2.                   reghdfe `v' F7event-L7event F7event_int-L7event_int F1event F1event_int c.(
> ${ctr})##c.idset $wgt if smpl_trim ==1, ///
>                         absorb(i.wahl_id##i.stadtbez##idset i.sb_new##idset) cluster(sb_new)
  3.                 
.                 estimates store `v'
  4.                                         
.                 // PLOT: DIFFERENCES
.                 event_plot  `v', ///
>                 stub_lag(L#event_int ) stub_lead(F#event_int ) plottype(connect) ciplottype(rcap
> ) ///
>                 together  trimlead(4) trimlag(2)  noautolegend ///
>                 graph_opt(ytitle("Difference between estimates", size(medsmall)) xlabel(-4(1)2) 
> xtitle("Election since reassignment", size(medsmall)) ///
>                         legend(pos(12) order(1 "Difference between estimates" ) row(1) region(st
> yle(none)) size(medsmall) title(" ")) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         name(`v',replace) ) ///
>                 lag_opt1(msymbol(T) msize(2.5pt) color(black) lcol(gray))       lag_ci_opt1(colo
> r(black))               
  5.         }
(MWFE estimator converged in 8 iterations)
note: idset is probably collinear with the fixed effects (all partialled-out values are close to z
> ero; tol = 1.0e-09)
note: F1event omitted because of collinearity
note: F1event_int omitted because of collinearity

HDFE Linear regression                            Number of obs   =      9,332
Absorbing 2 HDFE groups                           F(  56,    617) =       9.24
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9583
                                                  Adj R-squared   =     0.9491
                                                  Within R-sq.    =     0.0778
Number of clusters (sb_new)  =        618         Root MSE        =     0.9647

                                              (Std. err. adjusted for 618 clusters in sb_new)
---------------------------------------------------------------------------------------------
                            |               Robust
                      y_rel | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
                    F7event |  -.5209948   .2303738    -2.26   0.024    -.9734067    -.068583
                    F6event |  -.3089714   .2129139    -1.45   0.147    -.7270953    .1091524
                    F5event |  -.2704902   .1454133    -1.86   0.063    -.5560553    .0150748
                    F4event |  -.1508522   .1058163    -1.43   0.154    -.3586559    .0569516
                    F3event |  -.1724258   .1010701    -1.71   0.089     -.370909    .0260573
                    F2event |  -.0693473   .0760942    -0.91   0.362    -.2187825    .0800878
                    L0event |  -.3219776   .1103842    -2.92   0.004    -.5387518   -.1052033
                    L1event |  -.3311585   .1299782    -2.55   0.011    -.5864119   -.0759051
                    L2event |  -.2975536   .1258335    -2.36   0.018    -.5446674   -.0504397
                    L3event |  -.0966453   .1282831    -0.75   0.452    -.3485697    .1552791
                    L4event |  -.4948078   .2806502    -1.76   0.078    -1.045953    .0563377
                    L5event |  -.2202776   .2155885    -1.02   0.307    -.6436538    .2030986
                    L6event |   -.410378   .2175911    -1.89   0.060    -.8376869    .0169309
                    L7event |  -.5493126   .6805528    -0.81   0.420    -1.885793    .7871681
                F7event_int |   .1624512   .3218504     0.50   0.614    -.4696039    .7945063
                F6event_int |   .1037883   .3040855     0.34   0.733    -.4933797    .7009562
                F5event_int |   .4390879   .1922668     2.28   0.023     .0615112    .8166646
                F4event_int |   .0198449   .1424602     0.14   0.889    -.2599207    .2996104
                F3event_int |    .173284   .1505151     1.15   0.250       -.1223     .468868
                F2event_int |   .1119694    .110926     1.01   0.313    -.1058689    .3298076
                L0event_int |   .1320607   .1487696     0.89   0.375    -.1600954    .4242167
                L1event_int |   .1932396   .1579643     1.22   0.222    -.1169733    .5034525
                L2event_int |   .3319239   .1706018     1.95   0.052    -.0031066    .6669544
                L3event_int |   .1924234   .1604612     1.20   0.231    -.1226929    .5075396
                L4event_int |   .4044607   .4662419     0.87   0.386    -.5111527    1.320074
                L5event_int |  -.2457955   .4482385    -0.55   0.584    -1.126054    .6344626
                L6event_int |   1.207519   .4770554     2.53   0.012     .2706696    2.144368
                L7event_int |   1.255691   .6250046     2.01   0.045     .0282969    2.483085
                    F1event |          0  (omitted)
                F1event_int |          0  (omitted)
                  ln_ew_ges |  -.6166961   .4235781    -1.46   0.146    -1.448526    .2151334
                   ew_biodt |   .1414315   .0150628     9.39   0.000     .1118508    .1710121
                  ew_dtmihi |   .0313424   .0272736     1.15   0.251    -.0222179    .0849027
                   ew_ledig |   .0242754   .0334654     0.73   0.468    -.0414444    .0899953
                 ew_married |   .1263046   .0338263     3.73   0.000      .059876    .1927332
                  wb_anteil |  -.1037743    .012436    -8.34   0.000    -.1281963   -.0793522
                    wb_ausl |    .058835   .0097924     6.01   0.000     .0396046    .0780654
                   wb_18t24 |  -.0334301   .0167607    -1.99   0.047     -.066345   -.0005152
                   wb_25t34 |  -.0112159   .0114928    -0.98   0.329    -.0337857    .0113539
                   wb_35t44 |   .0296923   .0141506     2.10   0.036     .0019032    .0574814
                   wb_45t59 |   .0005441   .0135461     0.04   0.968    -.0260578    .0271461
                    avg_dur |  -.0115958   .0114853    -1.01   0.313    -.0341509    .0109593
                    hh_kids |  -.0417512   .0229997    -1.82   0.070    -.0869185     .003416
          mpreis_flats_rent |   .0336811   .0139973     2.41   0.016     .0061929    .0611692
                      idset |          0  (omitted)
                            |
        c.ln_ew_ges#c.idset |  -.2368198   .6565961    -0.36   0.718    -1.526254    1.052614
                            |
         c.ew_biodt#c.idset |  -.0888729   .0205957    -4.32   0.000    -.1293191   -.0484266
                            |
        c.ew_dtmihi#c.idset |  -.0244544   .0338264    -0.72   0.470    -.0908831    .0419744
                            |
         c.ew_ledig#c.idset |   -.078785   .0382848    -2.06   0.040    -.1539694   -.0036006
                            |
       c.ew_married#c.idset |  -.1095208   .0379457    -2.89   0.004    -.1840392   -.0350024
                            |
        c.wb_anteil#c.idset |   .0243784   .0151397     1.61   0.108    -.0053533    .0541101
                            |
          c.wb_ausl#c.idset |  -.1133271   .0145463    -7.79   0.000    -.1418934   -.0847608
                            |
         c.wb_18t24#c.idset |   .0547711   .0219902     2.49   0.013     .0115864    .0979557
                            |
         c.wb_25t34#c.idset |   .0135937   .0147661     0.92   0.358    -.0154042    .0425916
                            |
         c.wb_35t44#c.idset |  -.0042913   .0179271    -0.24   0.811    -.0394968    .0309143
                            |
         c.wb_45t59#c.idset |   .0393066   .0181945     2.16   0.031     .0035761    .0750372
                            |
          c.avg_dur#c.idset |  -.0008828   .0140535    -0.06   0.950    -.0284814    .0267157
                            |
          c.hh_kids#c.idset |   .0573961   .0292246     1.96   0.050     4.34e-06    .1147878
                            |
c.mpreis_flats_rent#c.idset |  -.0399623   .0182539    -2.19   0.029    -.0758095   -.0041151
                            |
                      _cons |    13.1831   3.435657     3.84   0.000     6.436103     19.9301
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------------------+
                Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------------------+---------------------------------------|
     wahl_id#stadtbez#idset |       400           1         399     |
               sb_new#idset |      1236        1236           0    *|
--------------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: idset is probably collinear with the fixed effects (all partialled-out values are close to z
> ero; tol = 1.0e-09)
note: F1event omitted because of collinearity
note: F1event_int omitted because of collinearity

HDFE Linear regression                            Number of obs   =      9,332
Absorbing 2 HDFE groups                           F(  56,    617) =       4.65
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9625
                                                  Adj R-squared   =     0.9542
                                                  Within R-sq.    =     0.0374
Number of clusters (sb_new)  =        618         Root MSE        =     2.3706

                                              (Std. err. adjusted for 618 clusters in sb_new)
---------------------------------------------------------------------------------------------
                            |               Robust
                      y_shr | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
                    F7event |  -.6881172   .4755768    -1.45   0.148    -1.622063    .2458283
                    F6event |  -.6169982   .4605276    -1.34   0.181     -1.52139    .2873933
                    F5event |  -1.137179   .3005647    -3.78   0.000    -1.727433   -.5469251
                    F4event |  -.2218534   .2254181    -0.98   0.325    -.6645332    .2208264
                    F3event |   -.303247   .2431721    -1.25   0.213    -.7807923    .1742983
                    F2event |   -.332789   .2135481    -1.56   0.120    -.7521583    .0865803
                    L0event |    -.30605   .2546026    -1.20   0.230    -.8060428    .1939427
                    L1event |  -.1929307   .2808633    -0.69   0.492    -.7444947    .3586332
                    L2event |  -.4154968   .3206343    -1.30   0.196    -1.045164      .21417
                    L3event |  -.4807208   .3555979    -1.35   0.177     -1.17905    .2176082
                    L4event |  -.3855642   .6696846    -0.58   0.565    -1.700702    .9295734
                    L5event |  -.6022141    .754102    -0.80   0.425    -2.083132    .8787037
                    L6event |  -1.932762   .8339126    -2.32   0.021    -3.570413   -.2951106
                    L7event |  -2.223212   1.621779    -1.37   0.171    -5.408088    .9616645
                F7event_int |   .5771806   .9601053     0.60   0.548     -1.30829    2.462651
                F6event_int |   .3915727   .9155818     0.43   0.669    -1.406462    2.189607
                F5event_int |    1.49265   .6132629     2.43   0.015     .2883143    2.696986
                F4event_int |   .2175533   .4350681     0.50   0.617    -.6368405    1.071947
                F3event_int |   .2131482   .4506546     0.47   0.636    -.6718546    1.098151
                F2event_int |   .3279345   .3879964     0.85   0.398    -.4340192    1.089888
                L0event_int |   .4765426    .464542     1.03   0.305    -.4357325    1.388818
                L1event_int |   .2997731   .5550322     0.54   0.589     -.790208    1.389754
                L2event_int |   .9952975   .5941942     1.68   0.094    -.1715907    2.162186
                L3event_int |   .8626194   .6782881     1.27   0.204    -.4694137    2.194653
                L4event_int |   .9603464   1.193131     0.80   0.421    -1.382743    3.303436
                L5event_int |   1.256228   1.585352     0.79   0.428    -1.857112    4.369568
                L6event_int |   2.855347   2.080703     1.37   0.170    -1.230771    6.941464
                L7event_int |   4.007876   2.198689     1.82   0.069    -.3099442    8.325697
                    F1event |          0  (omitted)
                F1event_int |          0  (omitted)
                  ln_ew_ges |  -.2228135   1.053432    -0.21   0.833     -2.29156    1.845933
                   ew_biodt |   .0688581   .0369971     1.86   0.063    -.0037973    .1415135
                  ew_dtmihi |   .0157346   .0594382     0.26   0.791    -.1009912    .1324604
                   ew_ledig |   .0422953   .0878764     0.48   0.630    -.1302778    .2148684
                 ew_married |   .1906535   .0844149     2.26   0.024     .0248782    .3564288
                  wb_anteil |  -.0353741   .0353381    -1.00   0.317    -.1047716    .0340234
                    wb_ausl |   .0432452   .0161164     2.68   0.007     .0115955    .0748948
                   wb_18t24 |  -.0738985   .0389541    -1.90   0.058    -.1503972    .0026003
                   wb_25t34 |  -.0473871   .0257484    -1.84   0.066    -.0979522    .0031779
                   wb_35t44 |   .0298552   .0337362     0.88   0.377    -.0363965     .096107
                   wb_45t59 |   -.086814    .032282    -2.69   0.007    -.1502099    -.023418
                    avg_dur |  -.0115818   .0252122    -0.46   0.646     -.061094    .0379303
                    hh_kids |  -.1417691   .0504101    -2.81   0.005    -.2407653   -.0427729
          mpreis_flats_rent |   .0387004   .0308761     1.25   0.211    -.0219346    .0993355
                      idset |          0  (omitted)
                            |
        c.ln_ew_ges#c.idset |   2.136469   2.160597     0.99   0.323    -2.106546    6.379485
                            |
         c.ew_biodt#c.idset |  -.0789611   .0720479    -1.10   0.274    -.2204499    .0625276
                            |
        c.ew_dtmihi#c.idset |  -.2062719   .1142443    -1.81   0.071    -.4306267    .0180829
                            |
         c.ew_ledig#c.idset |   .0237291   .1528862     0.16   0.877    -.2765114    .3239696
                            |
       c.ew_married#c.idset |  -.2361649   .1458412    -1.62   0.106    -.5225702    .0502403
                            |
        c.wb_anteil#c.idset |    .131328   .0719066     1.83   0.068    -.0098833    .2725393
                            |
          c.wb_ausl#c.idset |  -.1006462   .0306556    -3.28   0.001    -.1608482   -.0404441
                            |
         c.wb_18t24#c.idset |   .1332912   .0793691     1.68   0.094    -.0225752    .2891576
                            |
         c.wb_25t34#c.idset |   .0889106   .0540609     1.64   0.101    -.0172552    .1950763
                            |
         c.wb_35t44#c.idset |  -.1516752   .0677906    -2.24   0.026    -.2848035   -.0185469
                            |
         c.wb_45t59#c.idset |   .1402852   .0657998     2.13   0.033     .0110665    .2695039
                            |
          c.avg_dur#c.idset |   .0706881   .0496482     1.42   0.155    -.0268118     .168188
                            |
          c.hh_kids#c.idset |   .4793398   .0985736     4.86   0.000     .2857593    .6729202
                            |
c.mpreis_flats_rent#c.idset |     -.0674   .0590934    -1.14   0.254    -.1834485    .0486485
                            |
                      _cons |   27.95397   4.664351     5.99   0.000     18.79404     37.1139
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------------------+
                Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------------------+---------------------------------------|
     wahl_id#stadtbez#idset |       400           1         399     |
               sb_new#idset |      1236        1236           0    *|
--------------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
. }       

.         
.         * FIGURE 11. Effects of Reassignments on Party Outcomes (left-right): 4x Graph
.         graph combine turnout y_rel, row(1)             imargins(small) xcommon ycommon name(gr1
> , replace) ///
>                 title("{bf:Panel A.} Effect on Party Turnout", nobox span bexpand justification(
> left) size(medsmall) )

.         graph combine shares y_shr, row(1) imargins(small) xcommon ycommon name(gr2, replace) //
> /
>                 title("{bf:Panel B.} Effect on Party Vote Shares", nobox span bexpand justificat
> ion(left) size(medsmall) )

.         
.         graph combine gr1 gr2, xcommon col(1) imargins(small) iscale(.9)        

.         gr_edit .style.editstyle declared_ysize(4.2) editcopy   

.         graph export "$figures/Figure_11_ES_party_outc_two.pdf", replace                
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_11_ES_
    > party_outc_two.pdf saved as PDF format

.         
.         
. 
end of do-file
Running: 03d_het_by_distance_figures_8_9.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output: Figure 8, 9
> 
> Task: Heterogeneity by Increase/Decrease Distance (2 and 3 distance bins)
> 
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
. ********************************************************************************
. *        Prep Estimation *
. ********************************************************************************
.                                 
.         // relabel outcomes 
.         lab var turnout_urne    "Polling place turnout"

.         lab var turnout_pos_req "Mail-in turnout"

.         lab var turnout_tot_req "Total turnout"

.                 
.         // compute group ids for DISTANCE increase/decrease, 0 else
.         cap drop tmp*

.         assert del_street_dist!=0 if K==0

.         gen     tmp = (del_street_dist>0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase in event, 0 else"

.         
.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease in event, 0 else"

.         
.         tab ind_dist_up ind_dist_dn if K==0     

=1 if dist |
  increase |  =1 if dist decrease
 in event, |   in event, 0 else
    0 else |         0          1 |     Total
-----------+----------------------+----------
         0 |         0        108 |       108 
         1 |       172          0 |       172 
-----------+----------------------+----------
     Total |       172        108 |       280 

.         
.         
.         // compute group ids for DISTANCE: 3 groups
.         cap drop tmp*

.         _pctile del_street_dist                                                 if K==0 & del_st
> reet_dist<.7, n(3)

.         gen     tmp1 = (del_street_dist>r(r2))                  if K==0
(4,664 missing values generated)

.         bys sb_new (tmp1): gen ind_dist_m3 = tmp1[1]
(2,704 missing values generated)

.         replace ind_dist_m3 = 0                                                 if missing(Ei)
(2,704 real changes made)

.         
.         gen     tmp2 = (inrange(del_street_dist,r(r1),r(r2))) if K==0
(4,664 missing values generated)

.         bys sb_new (tmp2): gen ind_dist_m2 = tmp2[1]                                            
(2,704 missing values generated)

.         replace ind_dist_m2 = 0                                                 if missing(Ei)
(2,704 real changes made)

.         
.         gen     tmp3 = (del_street_dist<r(r1))                  if K==0
(4,664 missing values generated)

.         bys sb_new (tmp3): gen ind_dist_m1 = tmp3[1]
(2,704 missing values generated)

.         replace ind_dist_m1 = 0                                                 if missing(Ei)
(2,704 real changes made)

.         
.         su del_street_dist if ind_dist_m1 & K==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
del_street~t |         85   -.2890204    .2199019  -1.361774  -.0614609

.         su del_street_dist if ind_dist_m2 & K==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
del_street~t |         86     .056005    .0703781  -.0572766   .1863185

.         su del_street_dist if ind_dist_m3 & K==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
del_street~t |        109    .4840241      .23638   .1873118   1.091159

.         
.         
. ********************************************************************************
. *        Heterogeneity: Increase/Decrease Distance (2 bins), Figure 8
. ********************************************************************************        
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_dn                            // a 
> := decrease
  3.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b = F`l'event *ind_dist_up                         
>    // b:= increase
  5.                 lab var F`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6. 
.                 assert  F`l'event_b+F`l'event_a==F`l'event              
  7.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_dn    // a := decrease
  3.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b = L`l'event *ind_dist_up    // b:= increase
  5.                 lab var L`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 assert  L`l'event_b+L`l'event_a==L`l'event
  7.         }               

.         
.         // ORDER dummies
.         order *event_b, last

.         order F1event*,last     

.         
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         outreg,clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req del_street_dist treat_
> simple{
  2.         
.                         reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b
>  ///
>                                 $ctr $wgt if smpl_trim ==1, absorb(i.wahl_id#i.stadtbez i.sb_new
> ) cluster(sb_new)
  3.                                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2even
> t_b) store(`v')
  4. 
.                 estimates store `v'_a
  5.                 estimates store `v'_b   
  6.                 
.                 // PLOT
.                 event_plot  `v'_a `v'_b , ///
>                 stub_lag(L#event_a L#event_b ) stub_lead(F#event_a F#event_b ) plottype(connect)
>  ciplottype(rcap) ///
>                 together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(small)) xlab
> el(-4(1)2) xtitle("Election since reassignment") ///
>                         legend(pos(12) order(1 "Distance decrease" 3 "Distance increase" ) row(1
> ) region(style(none))) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lpat(solid) lcol(gra
> y)) ylabel(, angle(horizontal)) ///
>                         name(`v', replace)) ///
>                 lag_opt1(msymbol(O) msize(2.5pt) color(black)     lpat(-))      lag_ci_opt1(colo
> r(black)) ///
>                 lag_opt2(msymbol(S) msize(2.5pt) color(cranberry) lpat(solid)) lag_ci_opt2(color
> (cranberry))                            
  7.         }
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      14.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9734
                                                  Adj R-squared   =     0.9674
                                                  Within R-sq.    =     0.2120
Number of clusters (sb_new)  =        618         Root MSE        =     1.6565

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .3228052   .5620586     0.57   0.566    -.7809746    1.426585
        F6event_a |   -.008119   .5445209    -0.01   0.988    -1.077458     1.06122
        F5event_a |   .2401916    .384451     0.62   0.532    -.5147995    .9951827
        F4event_a |   -.156618   .2415581    -0.65   0.517    -.6309938    .3177578
        F3event_a |  -.2497959    .237662    -1.05   0.294    -.7165204    .2169286
        F2event_a |  -.2004224   .1774212    -1.13   0.259    -.5488451    .1480002
        L0event_a |    .475771   .3448323     1.38   0.168    -.2014161    1.152958
        L1event_a |   .6029534   .3142192     1.92   0.055    -.0141153    1.220022
        L2event_a |    .489146   .3473889     1.41   0.160     -.193062    1.171354
        L3event_a |    .768641   .3520682     2.18   0.029     .0772438    1.460038
        L4event_a |   .2930804   .6191775     0.47   0.636    -.9228704    1.509031
        L5event_a |   .5968234   1.001193     0.60   0.551    -1.369335    2.562982
        L6event_a |   2.971707   1.125918     2.64   0.009     .7606118    5.182803
        L7event_a |   .4542621    .595416     0.76   0.446    -.7150254     1.62355
        F7event_b |   -.458259   .4061302    -1.13   0.260    -1.255824    .3393062
        F6event_b |   .0370394   .3490613     0.11   0.916    -.6484527    .7225316
        F5event_b |   .1948103   .2958098     0.66   0.510    -.3861058    .7757263
        F4event_b |   .1010004   .2103875     0.48   0.631     -.312162    .5141628
        F3event_b |    .051904   .2046495     0.25   0.800    -.3499901     .453798
        F2event_b |   .1484136   .1455165     1.02   0.308    -.1373541    .4341812
        L0event_b |  -1.892486   .2685797    -7.05   0.000    -2.419927   -1.365045
        L1event_b |  -1.964155   .2722347    -7.21   0.000    -2.498774   -1.429536
        L2event_b |   -1.59154    .310441    -5.13   0.000    -2.201189   -.9818908
        L3event_b |  -1.046249   .3334843    -3.14   0.002    -1.701151   -.3913475
        L4event_b |  -1.513942   .5864355    -2.58   0.010    -2.665594   -.3622908
        L5event_b |  -1.192543   .6858851    -1.74   0.083    -2.539496     .154409
        L6event_b |  -.5363127   .5611732    -0.96   0.340    -1.638354    .5657283
        L7event_b |   1.781413   1.393593     1.28   0.202    -.9553482    4.518174
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -.9961214   .9212525    -1.08   0.280    -2.805292    .8130493
         ew_biodt |   .3683074   .0277532    13.27   0.000     .3138051    .4228097
        ew_dtmihi |   .0672716   .0499711     1.35   0.179    -.0308624    .1654056
         ew_ledig |   .2075291   .0540848     3.84   0.000     .1013165    .3137417
       ew_married |   .4088781   .0556124     7.35   0.000     .2996655    .5180906
        wb_anteil |   -.284197   .0201233   -14.12   0.000    -.3237154   -.2446785
          wb_ausl |    .016981   .0158374     1.07   0.284    -.0141208    .0480829
         wb_18t24 |  -.0166004   .0296128    -0.56   0.575    -.0747545    .0415537
         wb_25t34 |  -.0623142   .0187354    -3.33   0.001    -.0991071   -.0255213
         wb_35t44 |   .0057088   .0225147     0.25   0.800    -.0385059    .0499234
         wb_45t59 |   .0149036   .0214535     0.69   0.488    -.0272271    .0570344
          avg_dur |  -.0226264   .0201538    -1.12   0.262    -.0622046    .0169519
          hh_kids |  -.0391984   .0390032    -1.01   0.315    -.1157936    .0373968
mpreis_flats_rent |   .0286627   .0245902     1.17   0.244     -.019628    .0769534
            _cons |   13.43092   8.531997     1.57   0.116    -3.324354     30.1862
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      14.90
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9628
                                                  Adj R-squared   =     0.9544
                                                  Within R-sq.    =     0.2284
Number of clusters (sb_new)  =        618         Root MSE        =     1.6618

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.7726385   .5349539    -1.44   0.149     -1.82319    .2779128
        F6event_a |  -.1638495     .45516    -0.36   0.719      -1.0577    .7300012
        F5event_a |  -1.068847   .4042341    -2.64   0.008    -1.862688   -.2750054
        F4event_a |  -.2732057   .2186614    -1.25   0.212    -.7026165    .1562052
        F3event_a |   .1422311   .2194149     0.65   0.517    -.2886594    .5731216
        F2event_a |  -.0493139    .179667    -0.27   0.784    -.4021469    .3035192
        L0event_a |  -.4599664   .3083689    -1.49   0.136    -1.065546    .1456135
        L1event_a |  -.3946958   .3108984    -1.27   0.205    -1.005243    .2158516
        L2event_a |   .0490141   .3556253     0.14   0.890    -.6493687    .7473968
        L3event_a |  -.6600526   .3220275    -2.05   0.041    -1.292455   -.0276498
        L4event_a |  -.1714753   .9749698    -0.18   0.860    -2.086137    1.743186
        L5event_a |   1.330135   .8766554     1.52   0.130    -.3914553    3.051725
        L6event_a |  -2.832304   .6886765    -4.11   0.000    -4.184738    -1.47987
        L7event_a |  -1.841205   .6807652    -2.70   0.007    -3.178103   -.5043073
        F7event_b |   .5841825   .3298669     1.77   0.077    -.0636156    1.231981
        F6event_b |   .4327823   .2632287     1.64   0.101    -.0841505     .949715
        F5event_b |  -.1844277   .3115985    -0.59   0.554    -.7963498    .4274945
        F4event_b |  -.1933853   .1940332    -1.00   0.319    -.5744308    .1876602
        F3event_b |  -.0668334   .1799966    -0.37   0.711    -.4203135    .2866468
        F2event_b |  -.0883951   .1461363    -0.60   0.545      -.37538    .1985897
        L0event_b |   1.258426   .2583978     4.87   0.000     .7509797    1.765871
        L1event_b |   1.819219   .2672851     6.81   0.000      1.29432    2.344118
        L2event_b |   1.723036   .3262228     5.28   0.000     1.082394    2.363677
        L3event_b |   1.207218   .3296335     3.66   0.000     .5598788    1.854558
        L4event_b |   2.900775    .518063     5.60   0.000     1.883394    3.918155
        L5event_b |    2.93048   .5822414     5.03   0.000     1.787065    4.073896
        L6event_b |   1.816891   .6623496     2.74   0.006      .516158    3.117624
        L7event_b |    .546884   1.228547     0.45   0.656    -1.865757    2.959525
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.622643   1.331899     1.97   0.049      .007038    5.238248
         ew_biodt |    .390918   .0287073    13.62   0.000     .3345422    .4472938
        ew_dtmihi |  -.2363622   .0597435    -3.96   0.000    -.3536875   -.1190369
         ew_ledig |   .2132309   .0770102     2.77   0.006      .061997    .3644649
       ew_married |   .2221102   .0771453     2.88   0.004      .070611    .3736095
        wb_anteil |  -.2460859   .0218023   -11.29   0.000    -.2889017   -.2032702
          wb_ausl |  -.0699769    .014375    -4.87   0.000    -.0982067   -.0417471
         wb_18t24 |  -.0295899   .0273374    -1.08   0.279    -.0832755    .0240957
         wb_25t34 |   .0438136   .0190773     2.30   0.022     .0063493    .0812779
         wb_35t44 |  -.0081487    .024216    -0.34   0.737    -.0557044     .039407
         wb_45t59 |  -.0389038    .020268    -1.92   0.055    -.0787064    .0008987
          avg_dur |   .0429859   .0224788     1.91   0.056    -.0011584    .0871302
          hh_kids |  -.0731669   .0409186    -1.79   0.074    -.1535236    .0071898
mpreis_flats_rent |  -.0170848   .0235218    -0.73   0.468    -.0632773    .0291077
            _cons |  -12.97282   11.12696    -1.17   0.244    -34.82413     8.87849
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      33.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4440
Number of clusters (sb_new)  =        618         Root MSE        =     1.6210

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.4498347   .4925722    -0.91   0.361    -1.417156    .5174865
        F6event_a |  -.1719699   .3902489    -0.44   0.660     -.938347    .5944072
        F5event_a |   -.828655   .4505411    -1.84   0.066    -1.713435    .0561249
        F4event_a |  -.4298239   .2426357    -1.77   0.077    -.9063158     .046668
        F3event_a |  -.1075655   .2168072    -0.50   0.620    -.5333349     .318204
        F2event_a |  -.2497362   .2019212    -1.24   0.217    -.6462722    .1467999
        L0event_a |   .0158041    .239823     0.07   0.947    -.4551642    .4867724
        L1event_a |   .2082574   .2812297     0.74   0.459     -.344026    .7605409
        L2event_a |   .5381604   .3069533     1.75   0.080    -.0646395     1.14096
        L3event_a |   .1085883   .2874196     0.38   0.706     -.455851    .6730276
        L4event_a |   .1216059   .9220699     0.13   0.895     -1.68917    1.932382
        L5event_a |   1.926959   1.282873     1.50   0.134    -.5923686    4.446286
        L6event_a |   .1394028   1.303699     0.11   0.915    -2.420823    2.699628
        L7event_a |  -1.386941   .8623393    -1.61   0.108    -3.080417    .3065348
        F7event_b |   .1259241   .3695883     0.34   0.733    -.5998794    .8517276
        F6event_b |   .4698213   .3243514     1.45   0.148    -.1671453    1.106788
        F5event_b |   .0103831   .2850784     0.04   0.971    -.5494586    .5702248
        F4event_b |  -.0923843    .199051    -0.46   0.643    -.4832838    .2985153
        F3event_b |  -.0149294    .194926    -0.08   0.939    -.3977282    .3678695
        F2event_b |   .0600191   .1591538     0.38   0.706    -.2525298     .372568
        L0event_b |  -.6340596   .2007002    -3.16   0.002    -1.028198   -.2399212
        L1event_b |   -.144935    .252047    -0.58   0.565    -.6399089     .350039
        L2event_b |   .1314959    .278574     0.47   0.637    -.4155722    .6785641
        L3event_b |   .1609696   .3069884     0.52   0.600    -.4418993    .7638384
        L4event_b |   1.386832   .5405588     2.57   0.011     .3252744    2.448391
        L5event_b |   1.737936   .6427991     2.70   0.007     .4755971    3.000276
        L6event_b |   1.280575   .5287144     2.42   0.016     .2422772    2.318873
        L7event_b |   2.328298   .4952053     4.70   0.000     1.355806     3.30079
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.626521   1.066654     1.52   0.128    -.4681905    3.721233
         ew_biodt |   .7592254   .0315323    24.08   0.000     .6973017    .8211491
        ew_dtmihi |  -.1690904   .0518885    -3.26   0.001    -.2709899    -.067191
         ew_ledig |   .4207602   .0705442     5.96   0.000     .2822243    .5592962
       ew_married |   .6309884   .0690977     9.13   0.000     .4952933    .7666835
        wb_anteil |  -.5302829   .0240672   -22.03   0.000    -.5775465   -.4830192
          wb_ausl |  -.0529959   .0176265    -3.01   0.003     -.087611   -.0183807
         wb_18t24 |  -.0461903   .0262697    -1.76   0.079    -.0977791    .0053986
         wb_25t34 |  -.0185006   .0166385    -1.11   0.267    -.0511756    .0141745
         wb_35t44 |  -.0024399   .0210884    -0.12   0.908    -.0438536    .0389737
         wb_45t59 |  -.0240002    .019559    -1.23   0.220    -.0624104    .0144101
          avg_dur |   .0203595   .0222527     0.91   0.361    -.0233406    .0640597
          hh_kids |  -.1123654   .0358261    -3.14   0.002    -.1827212   -.0420096
mpreis_flats_rent |   .0115779   .0234248     0.49   0.621    -.0344241    .0575799
            _cons |   .4580862   9.951597     0.05   0.963    -19.08502     20.0012
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      11.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.4942
                                                  Adj R-squared   =     0.3802
                                                  Within R-sq.    =     0.3944
Number of clusters (sb_new)  =        618         Root MSE        =     0.0941

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  del_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.0185451   .0203215    -0.91   0.362    -.0584527    .0213626
        F6event_a |  -.0203159   .0189692    -1.07   0.285    -.0575679     .016936
        F5event_a |   .0044645   .0428169     0.10   0.917    -.0796201     .088549
        F4event_a |  -.0013863   .0119769    -0.12   0.908    -.0249067    .0221342
        F3event_a |  -.0025259   .0137111    -0.18   0.854     -.029452    .0244002
        F2event_a |   .0103331   .0130066     0.79   0.427    -.0152094    .0358756
        L0event_a |  -.2377763   .0258913    -9.18   0.000    -.2886221   -.1869305
        L1event_a |   .0162287   .0124108     1.31   0.191    -.0081439    .0406013
        L2event_a |   .0133971   .0121624     1.10   0.271    -.0104876    .0372819
        L3event_a |   .0278864   .0207236     1.35   0.179     -.012811    .0685838
        L4event_a |   .0302031   .0211427     1.43   0.154    -.0113173    .0717235
        L5event_a |  -.0014378   .0265654    -0.05   0.957    -.0536074    .0507318
        L6event_a |     .00352   .0244562     0.14   0.886    -.0445075    .0515474
        L7event_a |  -.0613498   .0534904    -1.15   0.252    -.1663952    .0436956
        F7event_b |  -.0030443   .0157045    -0.19   0.846     -.033885    .0277965
        F6event_b |  -.0085732   .0125294    -0.68   0.494    -.0331786    .0160322
        F5event_b |   .0000745   .0112572     0.01   0.995    -.0220325    .0221816
        F4event_b |  -.0040046   .0070412    -0.57   0.570    -.0178322    .0098229
        F3event_b |  -.0097829   .0083021    -1.18   0.239    -.0260866    .0065209
        F2event_b |   .0044755   .0066785     0.67   0.503    -.0086398    .0175908
        L0event_b |   .3318646   .0217738    15.24   0.000     .2891049    .3746242
        L1event_b |  -.0042994   .0132401    -0.32   0.745    -.0303004    .0217017
        L2event_b |  -.0109856   .0205888    -0.53   0.594    -.0514183    .0294471
        L3event_b |  -.1259928    .034064    -3.70   0.000    -.1928882   -.0590974
        L4event_b |  -.0016602   .0386866    -0.04   0.966    -.0776335    .0743131
        L5event_b |  -.0261538   .0350993    -0.75   0.456    -.0950824    .0427748
        L6event_b |  -.0284539   .0407422    -0.70   0.485    -.1084641    .0515563
        L7event_b |  -.0589497   .0398944    -1.48   0.140     -.137295    .0193956
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -.1135234    .053734    -2.11   0.035    -.2190471   -.0079997
         ew_biodt |  -.0023054   .0016598    -1.39   0.165     -.005565    .0009542
        ew_dtmihi |  -.0060938   .0027016    -2.26   0.024    -.0113992   -.0007884
         ew_ledig |  -.0000301   .0046826    -0.01   0.995    -.0092259    .0091657
       ew_married |  -.0044326   .0043611    -1.02   0.310     -.012997    .0041318
        wb_anteil |   .0009972    .001538     0.65   0.517    -.0020231    .0040176
          wb_ausl |  -.0001764   .0006407    -0.28   0.783    -.0014345    .0010818
         wb_18t24 |   -.000075   .0013195    -0.06   0.955    -.0026664    .0025163
         wb_25t34 |    .000666   .0008954     0.74   0.457    -.0010925    .0024245
         wb_35t44 |   -.001249   .0011682    -1.07   0.285    -.0035431    .0010452
         wb_45t59 |   .0012297   .0009237     1.33   0.184    -.0005842    .0030436
          avg_dur |  -.0006826   .0009972    -0.68   0.494    -.0026409    .0012757
          hh_kids |   .0046546   .0019522     2.38   0.017     .0008208    .0084884
mpreis_flats_rent |   .0003455   .0014237     0.24   0.808    -.0024504    .0031415
            _cons |   1.124208    .499207     2.25   0.025     .1438569    2.104559
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =     159.17
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7987
                                                  Adj R-squared   =     0.7533
                                                  Within R-sq.    =     0.6250
Number of clusters (sb_new)  =        618         Root MSE        =     0.1389

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     treat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.1032152   .0288027    -3.58   0.000    -.1597785   -.0466519
        F6event_a |  -.0932551   .0281994    -3.31   0.001    -.1486335   -.0378767
        F5event_a |  -.0590527   .0359218    -1.64   0.101    -.1295965     .011491
        F4event_a |  -.0361198   .0240559    -1.50   0.134    -.0833613    .0111216
        F3event_a |  -.0205013   .0256504    -0.80   0.424    -.0708739    .0298713
        F2event_a |  -.0360177   .0202506    -1.78   0.076    -.0757861    .0037507
        L0event_a |   .7853447   .0185903    42.24   0.000     .7488368    .8218527
        L1event_a |  -.0202749   .0199686    -1.02   0.310    -.0594897    .0189398
        L2event_a |  -.0274043   .0166756    -1.64   0.101    -.0601522    .0053436
        L3event_a |   .0778371   .0468698     1.66   0.097    -.0142065    .1698807
        L4event_a |  -.0538277   .0388926    -1.38   0.167    -.1302056    .0225502
        L5event_a |  -.1292399   .0478564    -2.70   0.007    -.2232212   -.0352587
        L6event_a |   .0043292   .0283043     0.15   0.878    -.0512551    .0599136
        L7event_a |  -.1085648   .1180641    -0.92   0.358    -.3404209    .1232914
        F7event_b |  -.0402212   .0300494    -1.34   0.181    -.0992326    .0187902
        F6event_b |  -.0477167   .0159908    -2.98   0.003    -.0791196   -.0163138
        F5event_b |  -.0862125   .0204972    -4.21   0.000    -.1264653   -.0459597
        F4event_b |  -.0415599   .0147103    -2.83   0.005    -.0704483   -.0126715
        F3event_b |  -.0331853   .0161209    -2.06   0.040    -.0648437   -.0015268
        F2event_b |  -.0340621    .012642    -2.69   0.007    -.0588886   -.0092355
        L0event_b |   .8125794   .0159138    51.06   0.000     .7813277    .8438312
        L1event_b |  -.0217619   .0182714    -1.19   0.234    -.0576436    .0141197
        L2event_b |   .0146121   .0246758     0.59   0.554    -.0338466    .0630708
        L3event_b |   .0985257   .0487912     2.02   0.044     .0027086    .1943427
        L4event_b |  -.0504544   .0595096    -0.85   0.397    -.1673204    .0664115
        L5event_b |    .018447   .0569054     0.32   0.746    -.0933047    .1301986
        L6event_b |   .0103437   .0279999     0.37   0.712    -.0446429    .0653303
        L7event_b |  -.1014721   .0578736    -1.75   0.080    -.2151252     .012181
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -.0417094   .0941501    -0.44   0.658    -.2266028     .143184
         ew_biodt |  -.0018182   .0024955    -0.73   0.467    -.0067189    .0030826
        ew_dtmihi |  -.0060403   .0037572    -1.61   0.108    -.0134188    .0013382
         ew_ledig |   .0063577   .0039043     1.63   0.104    -.0013097    .0140251
       ew_married |   .0023921   .0038734     0.62   0.537    -.0052144    .0099987
        wb_anteil |   .0011263   .0018946     0.59   0.552    -.0025944    .0048469
          wb_ausl |  -.0010159   .0008251    -1.23   0.219    -.0026362    .0006044
         wb_18t24 |  -.0032242   .0018252    -1.77   0.078    -.0068085    .0003601
         wb_25t34 |  -.0000868   .0010751    -0.08   0.936    -.0021981    .0020245
         wb_35t44 |   .0005308   .0016049     0.33   0.741    -.0026209    .0036825
         wb_45t59 |   .0003292   .0014133     0.23   0.816    -.0024463    .0031047
          avg_dur |   .0008552   .0013536     0.63   0.528     -.001803    .0035134
          hh_kids |   .0035189   .0029272     1.20   0.230    -.0022296    .0092674
mpreis_flats_rent |  -.0018463   .0018819    -0.98   0.327     -.005542    .0018493
            _cons |   .0848856   .8385281     0.10   0.919     -1.56183    1.731601
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         
.         // auxillary tests of L1event-L0event for mail-in and in-person when dist increased (tes
> t: waning vs. inattentive)
.         qui estimates restore turnout_pos_req_a

.         qui lincom L1event_b-L0event_b

.         di stritrim("Mail-in: tau1-tau0:`:di %12.2f `r(estimate)'', p=`:di %12.3f `r(p)''") 
Mail-in: tau1-tau0: 0.56, p= 0.007

.         qui estimates restore turnout_urne_a

.         qui lincom L1event_b-L0event_b

.         di stritrim("In-person: tau1-tau0:`:di %12.2f `r(estimate)'', p=`:di %12.3f `r(p)''") 
In-person: tau1-tau0: -0.07, p= 0.738

.                 
.         
.         * PLOT: FIGURE 8. Effect Heterogeneity by Change in Proximity to the Polling Location
.         grc1leg del_street_dist turnout_urne turnout_pos_req turnout_tot_req, col(2)  xcommon po
> s(6) imargins(small)

.         gr_edit .plotregion1.graph1.yaxis1.reset_rule -0.4 0.4 .2 , tickset(major) ruletype(rang
> e) 

.         gr_edit .plotregion1.graph1.yaxis1.title.text = {}

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"Change in distance in km"'

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"(estimates)"'

.                 gr_edit .plotregion1.graph1.title.text = {}

.                 gr_edit .plotregion1.graph1.title.text.Arrpush {bf:Panel A.} Change in Distance

.                 gr_edit .plotregion1.graph2.title.text = {}

.                 gr_edit .plotregion1.graph2.title.text.Arrpush {bf:Panel B.} Effect on Polling P
> lace Turnout

.                 gr_edit .plotregion1.graph3.title.text = {}

.                 gr_edit .plotregion1.graph3.title.text.Arrpush {bf:Panel C.} Effect on Mail-in T
> urnout

.                 gr_edit .plotregion1.graph4.title.text = {}

.                 gr_edit .plotregion1.graph4.title.text.Arrpush {bf:Panel D.} Effect on Total Tur
> nout    

.         gr_edit .style.editstyle declared_ysize(4) editcopy     

.         gr_edit .plotregion1.graph1.plotregion1.plot3.style.editstyle line(pattern(blank)) editc
> opy

.         gr_edit .plotregion1.graph1.plotregion1.plot1.style.editstyle line(pattern(blank)) editc
> opy

.         graph export "$figures/Figure_8_ES_het_by_distance.pdf", replace        
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_8_ES_h
    > et_by_distance.pdf saved as PDF format

.         
. 
.         
. ********************************************************************************
. *        Heterogeneity by distance, 3 bins, Figure 9
. ********************************************************************************                
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         
.         // Create two set of dummies: Reason Dummy x rel. time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_m1    // a := decrease
  3.                 gen             F`l'event_b = F`l'event *ind_dist_m2    // b:= middle
  4.                 gen     F`l'event_c = F`l'event *ind_dist_m3    // c := increase
  5.                 assert  F`l'event_b+F`l'event_a+F`l'event_c==F`l'event          
  6.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  7.                 lab var F`l'event_b "(N0)x\hspace{.7cm}Reassignment (#t-`l'#)"
  8.                 lab var F`l'event_c "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  9.                 
.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_m1    // a := decrease
  3.                 gen             L`l'event_b = L`l'event *ind_dist_m2    // b:= middle
  4.                 gen     L`l'event_c = L`l'event *ind_dist_m3    // c := increase
  5.                 assert  L`l'event_b+L`l'event_a+L`l'event_c==L`l'event  
  6.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  7.                 lab var L`l'event_b "(N0)x\hspace{.7cm}Reassignment (#t+`l'#)"
  8.                 lab var L`l'event_c "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"          
  9.                 
.         }               

.         
.         // ORDER dummies
.         order *event_b, last

.         order *event_c, last

.         order F1event*,last     

.         
.         lab var turnout_urne    "{bf:Panel B.} Effect on Polling Place Turnout"

.         lab var turnout_pos_req "{bf:Panel C.} Effect on Mail-in Turnout"

.         lab var turnout_tot_req "{bf:Panel D.} Effect on Total Turnout"

.         lab var del_street_dist "{bf:Panel A.} Change in Distance"      

.         
.         
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         outreg, clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req del_street_dist treat_
> simple{
  2.         
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F7event_c-L7event_c F1event
> _a F1event_b F1event_c ///
>                                 $ctr $wgt if smpl_trim ==1, absorb(i.wahl_id#i.stadtbez i.sb_new
> ) cluster(sb_new)               
  3.                                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2even
> t_b F4event_c-L2event_c) store(`v')
  4. 
.                 estimates store `v'_a
  5.                 estimates store `v'_b   
  6.                 estimates store `v'_c   
  7. 
.                 // Plot
.                 event_plot  `v'_a `v'_b `v'_c, ///
>                 stub_lag(L#event_a L#event_b L#event_c ) stub_lead(F#event_a F#event_b F#event_c
> ) plottype(scatter) ciplottype(rcap) ///
>                 together perturb(-0.15(0.15)0.15) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(small)) xlab
> el(-4(1)2) xtitle("Election since reassignment") ///
>                         legend(pos(12) order(1 "Distance decrease" 3 "Little distance change" 5 
> "Distance increase" ) row(1) region(style(none)) bexpand span justification(left)) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lpat(solid) lcol(gra
> y)) ylabel(, angle(horizontal)) ///
>                         title("`:var lab `v''", nobox span bexpand justification(left) size(medi
> um)) ///
>                         name(`v', replace)) ///
>                 lag_opt1(msymbol(O) msize(2.5pt) color(black))  lag_ci_opt1(color(black)) ///
>                 lag_opt2(msymbol(Dh) msize(2.5pt) color(gray)) lag_ci_opt2(color(gray)) ///
>                 lag_opt3(msymbol(S) msize(2.5pt) color(cranberry)) lag_ci_opt3(color(cranberry))
>                                 
  8. 
.         }
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  56,    617) =      17.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9742
                                                  Adj R-squared   =     0.9683
                                                  Within R-sq.    =     0.2358
Number of clusters (sb_new)  =        618         Root MSE        =     1.6343

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   1.017622   .5366414     1.90   0.058    -.0362427    2.071487
        F6event_a |   .6998389   .5337445     1.31   0.190    -.3483372    1.748015
        F5event_a |   .7064358   .4381569     1.61   0.107    -.1540239    1.566895
        F4event_a |  -.0632212   .2816427    -0.22   0.822    -.6163158    .4898733
        F3event_a |  -.2752338   .2849653    -0.97   0.334    -.8348533    .2843856
        F2event_a |  -.1722826    .192511    -0.89   0.371    -.5503389    .2057737
        L0event_a |   .8993293   .4021508     2.24   0.026     .1095789     1.68908
        L1event_a |   .9151499    .357627     2.56   0.011     .2128362    1.617464
        L2event_a |   .8537561   .3772853     2.26   0.024     .1128371    1.594675
        L3event_a |   .9326606   .3927798     2.37   0.018     .1613133    1.704008
        L4event_a |   1.082152   .6620151     1.63   0.103     -.217924    2.382228
        L5event_a |   1.904874   .9186455     2.07   0.039     .1008231    3.708925
        L6event_a |   3.572807   1.110231     3.22   0.001     1.392518    5.753097
        L7event_a |   1.036622   .5927024     1.75   0.081    -.1273364    2.200581
        F7event_b |  -1.157353    .525764    -2.20   0.028    -2.189857   -.1248495
        F6event_b |  -.6697205   .5200319    -1.29   0.198    -1.690968    .3515266
        F5event_b |   .1827155   .3873782     0.47   0.637    -.5780241    .9434551
        F4event_b |   .2149513   .2758557     0.78   0.436    -.3267786    .7566812
        F3event_b |    .004865   .2320969     0.02   0.983    -.4509306    .4606607
        F2event_b |    .008924   .1987647     0.04   0.964    -.3814135    .3992614
        L0event_b |  -.3991258   .3006287    -1.33   0.185    -.9895053    .1912538
        L1event_b |  -.7819364   .3133386    -2.50   0.013    -1.397276   -.1665969
        L2event_b |  -.3839879   .3567626    -1.08   0.282    -1.084604    .3166283
        L3event_b |   .0728486   .3487576     0.21   0.835    -.6120472    .7577445
        L4event_b |  -.8918905   .5953474    -1.50   0.135    -2.061043    .2772624
        L5event_b |  -.8916158   .7581562    -1.18   0.240    -2.380495    .5972636
        L6event_b |   .9451429   .4916294     1.92   0.055    -.0203268    1.910613
        L7event_b |    4.65583   .5272103     8.83   0.000     3.620485    5.691174
        F7event_c |    -.10684   .4727806    -0.23   0.821    -1.035294    .8216143
        F6event_c |   .1210504   .4228581     0.29   0.775    -.7093653    .9514661
        F5event_c |   .0409227   .3646903     0.11   0.911    -.6752619    .7571074
        F4event_c |  -.0721894   .2505257    -0.29   0.773    -.5641758     .419797
        F3event_c |   .0589843   .2639357     0.22   0.823     -.459337    .5773056
        F2event_c |   .1934686   .1834552     1.05   0.292    -.1668037    .5537409
        L0event_c |  -2.806215   .3217508    -8.72   0.000    -3.438075   -2.174356
        L1event_c |  -2.533749   .3440278    -7.36   0.000    -3.209357   -1.858142
        L2event_c |  -2.321825   .3836296    -6.05   0.000    -3.075203   -1.568447
        L3event_c |  -1.612523   .4492884    -3.59   0.000    -2.494843   -.7302031
        L4event_c |  -1.882039   1.039176    -1.81   0.071    -3.922789    .1587105
        L5event_c |  -1.131788   1.119181    -1.01   0.312    -3.329655    1.066079
        L6event_c |  -1.310978   .8097375    -1.62   0.106    -2.901154    .2791979
        L7event_c |  -.6094185    .626483    -0.97   0.331    -1.839716     .620879
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -.8868138   .9024813    -0.98   0.326    -2.659121    .8854936
         ew_biodt |   .3702864   .0274163    13.51   0.000     .3164457     .424127
        ew_dtmihi |   .0622572   .0493268     1.26   0.207    -.0346116    .1591259
         ew_ledig |   .2246273   .0527388     4.26   0.000      .121058    .3281965
       ew_married |   .4053943   .0538047     7.53   0.000     .2997317    .5110569
        wb_anteil |    -.28435   .0198202   -14.35   0.000    -.3232731   -.2454268
          wb_ausl |   .0204514   .0157863     1.30   0.196    -.0105499    .0514527
         wb_18t24 |  -.0033868   .0297567    -0.11   0.909    -.0618235    .0550499
         wb_25t34 |  -.0606317   .0188069    -3.22   0.001     -.097565   -.0236984
         wb_35t44 |   .0050624   .0217557     0.23   0.816    -.0376617    .0477866
         wb_45t59 |     .02102    .021459     0.98   0.328    -.0211215    .0631616
          avg_dur |  -.0204998   .0204824    -1.00   0.317    -.0607235    .0197239
          hh_kids |  -.0494108   .0391753    -1.26   0.208    -.1263439    .0275224
mpreis_flats_rent |   .0244055   .0245096     1.00   0.320     -.023727    .0725379
            _cons |   11.69877   8.421962     1.39   0.165    -4.840411    28.23796
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  56,    617) =      16.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9638
                                                  Adj R-squared   =     0.9555
                                                  Within R-sq.    =     0.2493
Number of clusters (sb_new)  =        618         Root MSE        =     1.6421

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -1.559129   .4596495    -3.39   0.001    -2.461796   -.6564622
        F6event_a |  -.8702494   .3679845    -2.36   0.018    -1.592903   -.1475954
        F5event_a |   -1.59304   .3818414    -4.17   0.000    -2.342906   -.8431733
        F4event_a |  -.3445564   .2387271    -1.44   0.149    -.8133726    .1242597
        F3event_a |   .2294548   .2436625     0.94   0.347    -.2490536    .7079632
        F2event_a |   -.111889   .2040947    -0.55   0.584    -.5126936    .2889155
        L0event_a |  -.6960456    .356652    -1.95   0.051    -1.396445    .0043533
        L1event_a |  -.5842502    .346639    -1.69   0.092    -1.264985     .096485
        L2event_a |  -.2646669   .3804157    -0.70   0.487    -1.011733    .4823996
        L3event_a |  -.8721279   .3503693    -2.49   0.013    -1.560189    -.184067
        L4event_a |  -1.050163   1.106086    -0.95   0.343    -3.222312    1.121986
        L5event_a |   1.008251   1.180946     0.85   0.394     -1.31091    3.327412
        L6event_a |  -3.198605   .7653624    -4.18   0.000    -4.701636   -1.695574
        L7event_a |   -2.18743    .694264    -3.15   0.002    -3.550837    -.824023
        F7event_b |   .6042734   .4920403     1.23   0.220    -.3620033     1.57055
        F6event_b |   .9826422   .4095074     2.40   0.017     .1784449    1.786839
        F5event_b |  -.8015146    .431034    -1.86   0.063    -1.647986     .044957
        F4event_b |  -.4888333   .2646757    -1.85   0.065    -1.008608    .0309412
        F3event_b |   .0071009    .263151     0.03   0.978    -.5096794    .5238811
        F2event_b |  -.1371056   .1953022    -0.70   0.483    -.5206432     .246432
        L0event_b |  -.0535669   .3031019    -0.18   0.860    -.6488033    .5416695
        L1event_b |   .4829375     .34113     1.42   0.157    -.1869792    1.152854
        L2event_b |   .2805788   .4076302     0.69   0.492    -.5199321     1.08109
        L3event_b |  -.0217239    .475521    -0.05   0.964    -.9555597    .9121119
        L4event_b |    2.16665   .6049169     3.58   0.000     .9787042    3.354595
        L5event_b |    1.78515   .6607448     2.70   0.007     .4875691    3.082732
        L6event_b |  -.1987419   .5234973    -0.38   0.704    -1.226794    .8293107
        L7event_b |  -2.302825   .5870697    -3.92   0.000    -3.455722   -1.149929
        F7event_c |   .8030678    .368596     2.18   0.030     .0792129    1.526923
        F6event_c |    .369296   .3066745     1.20   0.229    -.2329564    .9715483
        F5event_c |    .366508    .362693     1.01   0.313    -.3457543     1.07877
        F4event_c |   .0409174   .2215207     0.18   0.854    -.3941085    .4759434
        F3event_c |  -.1311407   .2100045    -0.62   0.533     -.543551    .2812696
        F2event_c |   -.013527   .1821977    -0.07   0.941    -.3713297    .3442758
        L0event_c |   2.041827    .298943     6.83   0.000     1.454758    2.628896
        L1event_c |   2.513704   .3101521     8.10   0.000     1.904622    3.122785
        L2event_c |   2.675139   .3808925     7.02   0.000     1.927136    3.423142
        L3event_c |   1.913533   .3638673     5.26   0.000     1.198964    2.628102
        L4event_c |   2.791987   .6181384     4.52   0.000     1.578077    4.005897
        L5event_c |    3.88078   .9595826     4.04   0.000     1.996337    5.765224
        L6event_c |   3.141249   .6297789     4.99   0.000     1.904479    4.378019
        L7event_c |   2.854822   .5827075     4.90   0.000     1.710491    3.999152
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   2.554313   1.307615     1.95   0.051    -.0136023    5.122228
         ew_biodt |   .3930139   .0279551    14.06   0.000     .3381153    .4479126
        ew_dtmihi |  -.2300266   .0584104    -3.94   0.000    -.3447338   -.1153194
         ew_ledig |   .1939967   .0725282     2.67   0.008     .0515647    .3364287
       ew_married |   .2247479   .0727447     3.09   0.002     .0818906    .3676052
        wb_anteil |  -.2475989   .0214305   -11.55   0.000    -.2896844   -.2055133
          wb_ausl |  -.0714843   .0144493    -4.95   0.000    -.0998601   -.0431084
         wb_18t24 |    -.03851   .0271986    -1.42   0.157     -.091923     .014903
         wb_25t34 |   .0427854   .0188646     2.27   0.024     .0057387     .079832
         wb_35t44 |  -.0055117    .023357    -0.24   0.814    -.0513807    .0403572
         wb_45t59 |  -.0440684   .0198865    -2.22   0.027    -.0831218   -.0050151
          avg_dur |    .038419   .0223518     1.72   0.086    -.0054758    .0823138
          hh_kids |  -.0646407   .0407355    -1.59   0.113    -.1446377    .0153564
mpreis_flats_rent |   -.012461   .0231708    -0.54   0.591    -.0579642    .0330422
            _cons |  -11.62911   10.86766    -1.07   0.285     -32.9712    9.712974
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  56,    617) =      27.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9905
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4469
Number of clusters (sb_new)  =        618         Root MSE        =     1.6198

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.5415084   .5920516    -0.91   0.361    -1.704189    .6211722
        F6event_a |  -.1704121   .4828486    -0.35   0.724    -1.118638    .7778139
        F5event_a |  -.8866034   .5099263    -1.74   0.083    -1.888005    .1147981
        F4event_a |   -.407778   .2786784    -1.46   0.144    -.9550512    .1394952
        F3event_a |  -.0457794   .2546407    -0.18   0.857    -.5458468    .4542881
        F2event_a |  -.2841713   .2379362    -1.19   0.233    -.7514342    .1830917
        L0event_a |   .2032831   .2718166     0.75   0.455    -.3305147    .7370809
        L1event_a |   .3308999   .3289168     1.01   0.315    -.3150323     .976832
        L2event_a |   .5890898   .3387366     1.74   0.083    -.0761267    1.254306
        L3event_a |   .0605326   .3042658     0.20   0.842    -.5369895    .6580546
        L4event_a |   .0319908   1.171724     0.03   0.978    -2.269059     2.33304
        L5event_a |   2.913125   1.902015     1.53   0.126    -.8220827    6.648332
        L6event_a |   .3742022    1.40973     0.27   0.791    -2.394248    3.142653
        L7event_a |  -1.150806   .8703116    -1.32   0.187    -2.859938    .5583261
        F7event_b |  -.5530807   .5054899    -1.09   0.274     -1.54577    .4396086
        F6event_b |    .312921   .4617352     0.68   0.498    -.5938422    1.219684
        F5event_b |  -.6187988   .4484084    -1.38   0.168     -1.49939    .2617928
        F4event_b |  -.2738815   .2564546    -1.07   0.286    -.7775112    .2297482
        F3event_b |   .0119653   .2451177     0.05   0.961    -.4694007    .4933314
        F2event_b |  -.1281813   .2200425    -0.58   0.560    -.5603043    .3039417
        L0event_b |  -.4526922   .2443943    -1.85   0.064    -.9326378    .0272533
        L1event_b |  -.2989983     .34056    -0.88   0.380    -.9677955    .3697989
        L2event_b |  -.1034099   .3688574    -0.28   0.779    -.8277781    .6209582
        L3event_b |   .0511257   .4009592     0.13   0.899    -.7362846    .8385359
        L4event_b |   1.274759    .540995     2.36   0.019     .2123439    2.337174
        L5event_b |   .8935358   .5127954     1.74   0.082    -.1135001    1.900572
        L6event_b |   .7463972   .5250032     1.42   0.156    -.2846127    1.777407
        L7event_b |   2.353006   .4926352     4.78   0.000     1.385561    3.320451
        F7event_c |    .696229   .3385615     2.06   0.040     .0313564    1.361102
        F6event_c |   .4903461   .3648417     1.34   0.179    -.2261359    1.206828
        F5event_c |   .4074311   .2842683     1.43   0.152    -.1508196    .9656817
        F4event_c |  -.0312713   .2402185    -0.13   0.896    -.5030163    .4404737
        F3event_c |  -.0721563   .2328961    -0.31   0.757    -.5295214    .3852089
        F2event_c |   .1799421   .1910984     0.94   0.347      -.19534    .5552243
        L0event_c |  -.7643874   .2627907    -2.91   0.004     -1.28046   -.2483147
        L1event_c |  -.0200455   .2970803    -0.07   0.946    -.6034566    .5633656
        L2event_c |   .3533143   .3479829     1.02   0.310    -.3300601    1.036689
        L3event_c |   .3010104    .385112     0.78   0.435    -.4552788      1.0573
        L4event_c |   .9099478   1.132033     0.80   0.422    -1.313157    3.133052
        L5event_c |   2.748988   1.810061     1.52   0.129    -.8056398    6.303616
        L6event_c |   1.830269   .7955074     2.30   0.022     .2680386    3.392499
        L7event_c |   2.245402   .7863268     2.86   0.004     .7012006    3.789603
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   1.667499   1.082865     1.54   0.124    -.4590486    3.794047
         ew_biodt |   .7633003    .031698    24.08   0.000     .7010512    .8255494
        ew_dtmihi |  -.1677692   .0518764    -3.23   0.001     -.269645   -.0658935
         ew_ledig |   .4186241   .0715848     5.85   0.000     .2780447    .5592035
       ew_married |   .6301423   .0699016     9.01   0.000     .4928685    .7674161
        wb_anteil |  -.5319489   .0241183   -22.06   0.000    -.5793128    -.484585
          wb_ausl |  -.0510329   .0175824    -2.90   0.004    -.0855614   -.0165044
         wb_18t24 |  -.0418968   .0261812    -1.60   0.110     -.093312    .0095183
         wb_25t34 |  -.0178464   .0167698    -1.06   0.288    -.0507792    .0150865
         wb_35t44 |  -.0004493   .0210376    -0.02   0.983    -.0417632    .0408647
         wb_45t59 |  -.0230484   .0196015    -1.18   0.240    -.0615422    .0154454
          avg_dur |   .0179192   .0221914     0.81   0.420    -.0256607     .061499
          hh_kids |  -.1140515   .0359698    -3.17   0.002    -.1846896   -.0434134
mpreis_flats_rent |   .0119445   .0235674     0.51   0.612    -.0343376    .0582266
            _cons |   .0696459   10.08968     0.01   0.994    -19.74463    19.88392
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  56,    617) =      25.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5938
                                                  Adj R-squared   =     0.5004
                                                  Within R-sq.    =     0.5136
Number of clusters (sb_new)  =        618         Root MSE        =     0.0845

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  del_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.0396095    .014983    -2.64   0.008    -.0690333   -.0101857
        F6event_a |  -.0401459   .0133378    -3.01   0.003     -.066339   -.0139529
        F5event_a |  -.0433399   .0136024    -3.19   0.002    -.0700524   -.0166273
        F4event_a |  -.0130109   .0097514    -1.33   0.183    -.0321608    .0061391
        F3event_a |  -.0107431   .0117794    -0.91   0.362    -.0338757    .0123894
        F2event_a |   .0001676   .0108012     0.02   0.988    -.0210441    .0213793
        L0event_a |  -.3103612    .024614   -12.61   0.000    -.3586986   -.2620238
        L1event_a |  -.0063252   .0092413    -0.68   0.494    -.0244734     .011823
        L2event_a |   .0092996   .0091608     1.02   0.310    -.0086906    .0272898
        L3event_a |   .0255252   .0194497     1.31   0.190    -.0126704    .0637208
        L4event_a |   .0172358   .0197626     0.87   0.383    -.0215742    .0560459
        L5event_a |  -.0145833   .0272186    -0.54   0.592    -.0680356     .038869
        L6event_a |  -.0229266   .0197118    -1.16   0.245     -.061637    .0157838
        L7event_a |  -.0911808   .0551161    -1.65   0.099    -.1994187    .0170571
        F7event_b |   .0059079   .0156233     0.38   0.705    -.0247734    .0365892
        F6event_b |   .0102601   .0176401     0.58   0.561    -.0243818     .044902
        F5event_b |    .025166    .039839     0.63   0.528    -.0530704    .1034025
        F4event_b |   .0050712   .0142703     0.36   0.722    -.0229532    .0330955
        F3event_b |   .0027706   .0155416     0.18   0.859    -.0277501    .0332914
        F2event_b |      .0112   .0141071     0.79   0.428    -.0165038    .0389038
        L0event_b |   .0598456   .0170023     3.52   0.000     .0264563     .093235
        L1event_b |   .0172784   .0153472     1.13   0.261    -.0128606    .0474174
        L2event_b |   .0284098   .0314064     0.90   0.366    -.0332667    .0900862
        L3event_b |  -.0281819   .0206362    -1.37   0.173    -.0687077    .0123438
        L4event_b |   .0295704   .0212771     1.39   0.165    -.0122139    .0713547
        L5event_b |  -.0277247   .0360099    -0.77   0.442    -.0984415    .0429921
        L6event_b |  -.0004826   .0187638    -0.03   0.979    -.0373312     .036366
        L7event_b |  -.0729248   .0416432    -1.75   0.080    -.1547044    .0088548
        F7event_c |  -.0163787   .0173674    -0.94   0.346    -.0504852    .0177277
        F6event_c |    -.01775   .0126174    -1.41   0.160    -.0425283    .0070282
        F5event_c |  -.0065875   .0107798    -0.61   0.541     -.027757     .014582
        F4event_c |  -.0092427    .007087    -1.30   0.193    -.0231603    .0046748
        F3event_c |  -.0143546   .0092405    -1.55   0.121    -.0325012     .003792
        F2event_c |   .0026139   .0072144     0.36   0.717    -.0115538    .0167816
        L0event_c |   .4708109   .0234317    20.09   0.000     .4247954    .5168264
        L1event_c |  -.0250784   .0166676    -1.50   0.133    -.0578105    .0076537
        L2event_c |  -.0360207   .0208782    -1.73   0.085    -.0770217    .0049803
        L3event_c |  -.1981009   .0468014    -4.23   0.000    -.2900104   -.1061915
        L4event_c |  -.1533795    .031027    -4.94   0.000    -.2143109   -.0924482
        L5event_c |  -.0838407   .0251437    -3.33   0.001    -.1332182   -.0344631
        L6event_c |  -.0967913   .0231798    -4.18   0.000    -.1423122   -.0512704
        L7event_c |  -.1005294   .0409319    -2.46   0.014    -.1809121   -.0201467
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -.1195378   .0502375    -2.38   0.018    -.2181951   -.0208805
         ew_biodt |  -.0030673   .0015678    -1.96   0.051    -.0061461    .0000115
        ew_dtmihi |  -.0054734   .0025118    -2.18   0.030    -.0104062   -.0005407
         ew_ledig |   .0007247   .0038163     0.19   0.849    -.0067699    .0082193
       ew_married |  -.0028365   .0035703    -0.79   0.427     -.009848     .004175
        wb_anteil |   .0011153   .0013712     0.81   0.416    -.0015774     .003808
          wb_ausl |  -.0005675   .0005638    -1.01   0.315    -.0016747    .0005397
         wb_18t24 |  -.0014485   .0010801    -1.34   0.180    -.0035696    .0006725
         wb_25t34 |   .0000621   .0007604     0.08   0.935    -.0014311    .0015553
         wb_35t44 |  -.0014571   .0010675    -1.36   0.173    -.0035535    .0006393
         wb_45t59 |   .0000657   .0008769     0.07   0.940    -.0016565    .0017878
          avg_dur |  -.0004094   .0009624    -0.43   0.671    -.0022994    .0014806
          hh_kids |   .0046121   .0018918     2.44   0.015      .000897    .0083271
mpreis_flats_rent |   .0006169   .0012731     0.48   0.628    -.0018833     .003117
            _cons |   1.154831   .4474859     2.58   0.010     .2760512    2.033612
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  56,    617) =     157.59
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7991
                                                  Adj R-squared   =     0.7529
                                                  Within R-sq.    =     0.6257
Number of clusters (sb_new)  =        618         Root MSE        =     0.1390

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     treat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.1088161   .0334022    -3.26   0.001    -.1744118   -.0432203
        F6event_a |  -.0986136     .03352    -2.94   0.003    -.1644408   -.0327865
        F5event_a |  -.0739871   .0408017    -1.81   0.070    -.1541141    .0061399
        F4event_a |  -.0234161   .0276133    -0.85   0.397    -.0776436    .0308114
        F3event_a |  -.0153746   .0286583    -0.54   0.592    -.0716543     .040905
        F2event_a |  -.0264968   .0235478    -1.13   0.261    -.0727403    .0197467
        L0event_a |   .7849584    .020791    37.75   0.000     .7441288    .8257881
        L1event_a |  -.0252663    .019599    -1.29   0.198    -.0637552    .0132226
        L2event_a |  -.0184107   .0177664    -1.04   0.300    -.0533007    .0164793
        L3event_a |   .0832645    .049253     1.69   0.091    -.0134593    .1799883
        L4event_a |  -.0597289   .0485432    -1.23   0.219    -.1550589    .0356012
        L5event_a |  -.0982695   .0532295    -1.85   0.065    -.2028025    .0062635
        L6event_a |   .0106652    .029578     0.36   0.719    -.0474206     .068751
        L7event_a |     -.1013   .1197731    -0.85   0.398    -.3365124    .1339123
        F7event_b |  -.0775364   .0294957    -2.63   0.009    -.1354605   -.0196124
        F6event_b |  -.0524814   .0230329    -2.28   0.023    -.0977138   -.0072489
        F5event_b |  -.0742438   .0301756    -2.46   0.014     -.133503   -.0149845
        F4event_b |  -.0700507   .0213599    -3.28   0.001    -.1119977   -.0281038
        F3event_b |   -.043426   .0281265    -1.54   0.123    -.0986612    .0118092
        F2event_b |  -.0552657   .0192586    -2.87   0.004     -.093086   -.0174454
        L0event_b |   .7937194   .0214694    36.97   0.000     .7515574    .8358815
        L1event_b |  -.0314296   .0253385    -1.24   0.215    -.0811897    .0183306
        L2event_b |  -.0404119    .033878    -1.19   0.233     -.106942    .0261183
        L3event_b |   .0591244   .0614529     0.96   0.336    -.0615578    .1798066
        L4event_b |  -.0241935   .0548255    -0.44   0.659    -.1318606    .0834737
        L5event_b |  -.0581042   .0733632    -0.79   0.429     -.202176    .0859675
        L6event_b |   -.032152   .0347389    -0.93   0.355    -.1003729    .0360688
        L7event_b |  -.1023758   .0711105    -1.44   0.150    -.2420237    .0372721
        F7event_c |   -.016616    .041127    -0.40   0.686    -.0973819    .0641499
        F6event_c |  -.0503737    .018307    -2.75   0.006    -.0863253   -.0144221
        F5event_c |  -.0851703   .0233126    -3.65   0.000    -.1309519   -.0393888
        F4event_c |  -.0280417   .0174757    -1.60   0.109    -.0623607    .0062773
        F3event_c |  -.0270413    .015946    -1.70   0.090    -.0583564    .0042738
        F2event_c |  -.0253565   .0143057    -1.77   0.077    -.0534502    .0027372
        L0event_c |   .8211648   .0178429    46.02   0.000     .7861246     .856205
        L1event_c |  -.0098277   .0235338    -0.42   0.676    -.0560437    .0363882
        L2event_c |   .0413711   .0295727     1.40   0.162    -.0167043    .0994465
        L3event_c |   .1190028   .0654452     1.82   0.069    -.0095196    .2475251
        L4event_c |  -.1783489   .0515146    -3.46   0.001    -.2795141   -.0771837
        L5event_c |  -.0030317   .0377984    -0.08   0.936    -.0772608    .0711974
        L6event_c |   .0117498   .0320948     0.37   0.714    -.0512784     .074778
        L7event_c |  -.1476916   .0691577    -2.14   0.033    -.2835046   -.0118786
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -.0385799    .094716    -0.41   0.684    -.2245847    .1474249
         ew_biodt |  -.0017505   .0025024    -0.70   0.484    -.0066647    .0031638
        ew_dtmihi |  -.0058582   .0037833    -1.55   0.122    -.0132879    .0015714
         ew_ledig |   .0061958   .0039451     1.57   0.117    -.0015516    .0139431
       ew_married |   .0025081   .0039034     0.64   0.521    -.0051574    .0101736
        wb_anteil |   .0010713   .0018986     0.56   0.573    -.0026571    .0047998
          wb_ausl |  -.0010423   .0008299    -1.26   0.210    -.0026721    .0005875
         wb_18t24 |  -.0032837    .001841    -1.78   0.075     -.006899    .0003317
         wb_25t34 |  -.0002052   .0010753    -0.19   0.849    -.0023169    .0019065
         wb_35t44 |   .0006667    .001618     0.41   0.680    -.0025108    .0038441
         wb_45t59 |   .0002089   .0014173     0.15   0.883    -.0025743    .0029921
          avg_dur |   .0009427   .0013767     0.68   0.494    -.0017608    .0036463
          hh_kids |   .0035643   .0029216     1.22   0.223    -.0021731    .0093017
mpreis_flats_rent |  -.0019539   .0018934    -1.03   0.303    -.0056722    .0017645
            _cons |   .0641323   .8460429     0.08   0.940     -1.59734    1.725605
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         *       PLOT: FIGURE 9. Effect Heterogeneity by Change in Proximity to the Polling Locat
> ion
.         grc1leg del_street_dist turnout_urne turnout_pos_req turnout_tot_req, col(2)  xcommon po
> s(6) imargins(small)

.         gr_edit .plotregion1.graph1.yaxis1.reset_rule -0.6 0.6 .3 , tickset(major) ruletype(rang
> e) 

.         gr_edit .plotregion1.graph1.yaxis1.title.text = {}

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"Change in distance in km"'

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"(estimates)"'

.         gr_edit .style.editstyle declared_ysize(4) editcopy

.         graph export "$figures/Figure_9_ES_het_by_distance_3clust.pdf", replace 
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_9_ES_h
    > et_by_distance_3clust.pdf saved as PDF format

. 
end of do-file
Running: 03e_het_by_precinct_characs_figures_10_d13_tables_e6_e7.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output:
>         - Figure 10, D.13
>         - Table E.6
>         - Table E.7
>         
> Tasks:  
>         * Triple Diff estimator: Heterogeneity by Precinct Characteristic
>         * Robustness: DDD conditional on log walking distance
>         * Correlation matrix of heterog. dimensions (2013)
>         * Export figure and table
>         
> 
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
. ********************************************************************************
.  //      Prep Estimation //
. ********************************************************************************
.         
.         // clone variable b/c varname is too long for outreg below
.         clonevar mpreis_rent=mpreis_flats_rent

.         
.         // relabel variable for Figure header
.         lab var turnout_urne      "Polling place turnout"

.                 
.         // Standardize variables as of 2013 (mean zero, unit SD), gen: z_*
.         foreach v of varlist $ctr wb_60plus mpreis_rent {
  2.                 zscore `v' if wahl_id==1                                                // LT
> W13
  3.                 bys sb_new (wahl_id): replace z_`v'=z_`v'[1]    //time constant as of 2013
  4.                 local lb: var lab `v'
  5.                 local lb_n=subinstr("`lb'","\%","%",.)
  6. 
.                 lab var z_`v' "`lb_n'"
  7.         }
z_ln_ew_ges created with 4326 missing values
(4326 real changes made)
z_ew_biodt created with 4326 missing values
(4326 real changes made)
z_ew_dtmihi created with 4326 missing values
(4326 real changes made)
z_ew_ledig created with 4326 missing values
(4326 real changes made)
z_ew_married created with 4326 missing values
(4326 real changes made)
z_wb_anteil created with 4326 missing values
(4326 real changes made)
z_wb_ausl created with 4944 missing values
(0 real changes made)
z_wb_18t24 created with 4326 missing values
(4326 real changes made)
z_wb_25t34 created with 4326 missing values
(4326 real changes made)
z_wb_35t44 created with 4326 missing values
(4326 real changes made)
z_wb_45t59 created with 4326 missing values
(4326 real changes made)
z_avg_dur created with 4326 missing values
(4326 real changes made)
z_hh_kids created with 4326 missing values
(4326 real changes made)
z_mpreis_flats_rent created with 4326 missing values
(4326 real changes made)
z_wb_60plus created with 4326 missing values
(4326 real changes made)
z_mpreis_rent created with 4326 missing values
(4326 real changes made)

.         
.         
.         // set global containing vars for het analysis
.         global het z_wb_60plus z_wb_18t24 z_hh_kids z_mpreis_rent z_ew_dtmihi z_avg_dur 

.         
. ********************************************************************************
.  // Corrs + Sumstats among vars used in heterogeneity, 2013, N=618 (Table E7) //
. ********************************************************************************
. 
. * TABLE E7. Summary Statistics and Correlations among Precinct Characteristics, 2013
. pwcorr wb_60plus wb_18t24 hh_kids mpreis_rent ew_dtmihi  avg_dur if wahl_id==1, star(.05)

             | wb_60p~s wb_18t24  hh_kids mpreis.. ew_dtm~i  avg_dur
-------------+------------------------------------------------------
   wb_60plus |   1.0000 
    wb_18t24 |  -0.3139*  1.0000 
     hh_kids |   0.0255   0.0734   1.0000 
 mpreis_rent |  -0.2548* -0.0349  -0.3392*  1.0000 
   ew_dtmihi |   0.1394*  0.1800*  0.5642* -0.2846*  1.0000 
     avg_dur |   0.6611* -0.1722*  0.0965* -0.2798*  0.1830*  1.0000 

. mkcorr wb_60plus wb_18t24 hh_kids mpreis_rent ew_dtmihi  avg_dur if wahl_id==1, mdec(3) cdec(3) 
> ///
>         means replace log("${tables}/Table_E7_corrs_het_dimensions_2013") sig
(file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_E7_corrs
    > _het_dimensions_2013.log not found)

. 
.         
. 
. ********************************************************************************
.  // Heterogeneity: DDD by 2013 precinct characteristics //
. ********************************************************************************                
.         
.         // Labels for figure
.         local j=1

.         foreach v of varlist $het {
  2.                 local lb: var lab `v'
  3.                 local x = "`: word `j' of `c(alpha)''"
  4.                 lab var `v' "(`x') `lb'"
  5.                 local ++j
  6.         }

.         
. estimates clear

. outreg, clear

. foreach h of varlist $het  {
  2.         
.         // gen leads and lags
.         cap drop L* F*
  3.         forvalues l = 7(-1)1 {
  4.                 gen F`l'event = K==-`l'
  5.                 gen F`l'event_n = F`l'event * `h'
  6.                 lab var F`l'event_n "\hspace{.7cm}Reassignment (#t-`l'#)"               
  7.         }       
  8.         forvalues l = 0/7 {
  9.                 gen L`l'event = K==`l'
 10.                 gen L`l'event_n = L`l'event * `h'
 11.                 lab var L`l'event_n "\hspace{.7cm}Reassignment (#t+`l'#)"
 12.         }
 13.         order *event_n, last
 14.         order F1event*, last
 15.         
.         // Estimate ES: base levels + interactions
.         foreach v in urne pos_req tot_req{
 16.         
.                  reghdfe turnout_`v' F7event-L7event F7event_n-L7event_n F1event F1event_n $ctr 
> $wgt if smpl_trim ==1, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
 17.                 estimates store `h'_`v'
 18.                 qui outreg,  $opt  keep(F4event_n-L2event_n) store(`v'_`h') ctitle("","`:var 
> lab `h''")
 19.         }
 20.         
.                 // PLOT: All 3 outcomes in one plot
.                 event_plot  `h'_urne `h'_pos_req `h'_tot_req , ///
>                 stub_lag(L#event_n ) stub_lead(F#event_n ) plottype(scatter) ciplottype(rcap) //
> /
>                 together perturb(-0.23(0.23)0.23) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(small)) xlab
> el(-4(1)2) xtitle("Election since reassignment", size(small)) ///
>                         legend(pos(12) order(1 "Polling place turnout" 3 "Mail-in turnout" 5 "To
> tal turnout" ) size(vsmall) col(1) region(style(none)) title("{bf:Outcomes:}", pos(11) just(left
> ) span bexpand size(vsmall))) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         title("`:var lab `h''", size(small)) ///
>                         name(`h', replace) ) ///
>                 lag_opt1(msymbol(S) msize(2.5pt) color(navy))           lag_ci_opt1(color(navy))
>  ///
>                 lag_opt2(msymbol(O) msize(2.5pt) color(maroon))         lag_ci_opt2(color(maroon
> )) ///  
>                 lag_opt3(msymbol(Oh) msize(2.5pt) color(black))         lag_ci_opt3(color(black)
> )               
 21.                 
.                 
.                 // PLOT: 2 outcomes (mail, pp)
.                 event_plot  `h'_urne `h'_pos_req /*`h'_tot_req*/ , ///
>                 stub_lag(L#event_n ) stub_lead(F#event_n ) plottype(connect) ciplottype(rcap) //
> /
>                 together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(ytitle("Voter turnout in %""(estimates)", size(small)) xlabel(-4(1)2) 
> xtitle("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) order(1 "Polling place turnout" 3 "Mail-in turnout") row(1) regio
> n(style(none))) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         name(`h'_urne_pos, replace) ) ///
>                 lag_opt1(msymbol(S) msize(2.5pt) color(navy))   lag_ci_opt1(color(navy)) ///
>                 lag_opt2(msymbol(O) msize(2.5pt) color(maroon)) lag_ci_opt2(color(maroon)) 
 22. 
.                 // PLOT: 1 outcome (overall)
.                 event_plot  `h'_tot_req, ///
>                 stub_lag(L#event_n ) stub_lead(F#event_n ) plottype(connect) ciplottype(rcap) //
> /
>                 together trimlead(4) trimlag(2)  noautolegend ///
>                 graph_opt(ytitle("Voter turnout in %""(estimates)", size(small)) xlabel(-4(1)2) 
> xtitle("Election since reassignment", size(medsmall)) ///
>                         legend(pos(11) order(1 "Total turnout") row(1) region(style(none))) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         name(`h'_tot_req,replace) ) ///
>                 lag_opt1(msymbol(Oh) msize(2.5pt) color(black)) lag_ci_opt1(color(black)) 
 23. }
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      18.28
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9723
                                                  Adj R-squared   =     0.9660
                                                  Within R-sq.    =     0.1775
Number of clusters (sb_new)  =        618         Root MSE        =     1.6924

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1989159   .3614336    -0.55   0.582    -.9087051    .5108734
          F6event |    .016347   .3302055     0.05   0.961     -.632116      .66481
          F5event |   .2230714   .2553647     0.87   0.383     -.278418    .7245608
          F4event |   .0006663   .1749895     0.00   0.997    -.3429809    .3443135
          F3event |    -.06477   .1718346    -0.38   0.706    -.4022216    .2726816
          F2event |   .0136022    .122554     0.11   0.912    -.2270713    .2542756
          L0event |  -1.055917   .2375919    -4.44   0.000    -1.522504   -.5893303
          L1event |  -.9234741   .2363906    -3.91   0.000    -1.387702   -.4592464
          L2event |  -.7210527   .2590821    -2.78   0.006    -1.229842   -.2122631
          L3event |  -.2559707   .2679268    -0.96   0.340    -.7821296    .2701883
          L4event |  -.9263554    .524925    -1.76   0.078    -1.957212    .1045009
          L5event |  -.9317636   .6110033    -1.52   0.128    -2.131662    .2681346
          L6event |   1.066649   .7906674     1.35   0.178    -.4860769    2.619374
          L7event |   .7210908   .7612643     0.95   0.344    -.7738924    2.216074
        F7event_n |   .1195679   .3664434     0.33   0.744    -.6000597    .8391955
        F6event_n |   .1907914   .3141033     0.61   0.544    -.4260498    .8076326
        F5event_n |   .2272421   .2196752     1.03   0.301    -.2041596    .6586437
        F4event_n |   .2426845   .1664863     1.46   0.145    -.0842639     .569633
        F3event_n |   .2232852     .17466     1.28   0.202     -.119715    .5662854
        F2event_n |   .1893176   .1200497     1.58   0.115    -.0464379    .4250731
        L0event_n |  -.4261453   .2274397    -1.87   0.061     -.872795    .0205044
        L1event_n |  -.4873064   .2095102    -2.33   0.020    -.8987458   -.0758669
        L2event_n |   -.166733   .2570326    -0.65   0.517    -.6714979    .3380319
        L3event_n |  -.0992356   .2847697    -0.35   0.728    -.6584711    .4599999
        L4event_n |   -.391112   .5151295    -0.76   0.448    -1.402732    .6205077
        L5event_n |  -.7186851   .6548059    -1.10   0.273    -2.004603    .5672334
        L6event_n |  -.2695278   .6199956    -0.43   0.664    -1.487085    .9480296
        L7event_n |  -3.872088   1.426743    -2.71   0.007    -6.673949   -1.070228
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   -1.02811   .9572176    -1.07   0.283     -2.90791     .851689
         ew_biodt |   .3691926   .0280538    13.16   0.000        .3141    .4242852
        ew_dtmihi |   .0633032   .0506993     1.25   0.212    -.0362609    .1628672
         ew_ledig |   .1950332   .0570388     3.42   0.001     .0830195    .3070468
       ew_married |   .4051995   .0578571     7.00   0.000     .2915789    .5188202
        wb_anteil |   -.287769   .0204114   -14.10   0.000    -.3278532   -.2476847
          wb_ausl |   .0140254   .0163814     0.86   0.392    -.0181446    .0461954
         wb_18t24 |  -.0135183    .028994    -0.47   0.641    -.0704571    .0434206
         wb_25t34 |  -.0584031   .0189251    -3.09   0.002    -.0955686   -.0212376
         wb_35t44 |   .0150393   .0231673     0.65   0.516     -.030457    .0605356
         wb_45t59 |   .0221867   .0217478     1.02   0.308     -.020522    .0648954
          avg_dur |  -.0312802   .0205646    -1.52   0.129    -.0716654     .009105
          hh_kids |  -.0414282   .0393312    -1.05   0.293    -.1186675    .0358111
mpreis_flats_rent |   .0236679   .0248628     0.95   0.342    -.0251581    .0724938
            _cons |   14.56662   8.875254     1.64   0.101    -2.862751    31.99598
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      54.97
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9618
                                                  Adj R-squared   =     0.9532
                                                  Within R-sq.    =     0.2092
Number of clusters (sb_new)  =        618         Root MSE        =     1.6823

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0797846   .3287541     0.24   0.808    -.5658281    .7253972
          F6event |   .2217923   .2634895     0.84   0.400    -.2956527    .7392373
          F5event |  -.4930109   .2646466    -1.86   0.063    -1.012728    .0267064
          F4event |  -.2582434   .1601308    -1.61   0.107    -.5727109     .056224
          F3event |  -.0274917   .1535347    -0.18   0.858    -.3290056    .2740222
          F2event |  -.0525889   .1256414    -0.42   0.676    -.2993255    .1941477
          L0event |   .5791002   .2178868     2.66   0.008     .1512106     1.00699
          L1event |   .8789629   .2286119     3.84   0.000     .4300112    1.327915
          L2event |   1.047381   .2584203     4.05   0.000     .5398911    1.554871
          L3event |   .4074361   .2559288     1.59   0.112    -.0951611    .9100334
          L4event |   .7111452   .8080326     0.88   0.379    -.8756824    2.297973
          L5event |    1.47081   .4865142     3.02   0.003     .5153851    2.426234
          L6event |  -.7663837   .7263705    -1.06   0.292    -2.192842    .6600744
          L7event |  -.7195406   .7288197    -0.99   0.324    -2.150809    .7117275
        F7event_n |   .2125015   .2677552     0.79   0.428    -.3133205    .7383235
        F6event_n |   .1818453   .2270729     0.80   0.424    -.2640842    .6277748
        F5event_n |   .0876412   .2328609     0.38   0.707    -.3696548    .5449372
        F4event_n |  -.0251784   .1604666    -0.16   0.875    -.3403054    .2899486
        F3event_n |  -.2086688   .1710229    -1.22   0.223    -.5445263    .1271886
        F2event_n |   .0840326   .1275243     0.66   0.510    -.1664017    .3344669
        L0event_n |  -.2271153   .1971751    -1.15   0.250    -.6143309    .1601004
        L1event_n |   -.282124   .2170072    -1.30   0.194    -.7082864    .1440383
        L2event_n |  -.5838934   .2308054    -2.53   0.012    -1.037153   -.1306341
        L3event_n |  -.3127846   .2692228    -1.16   0.246    -.8414886    .2159195
        L4event_n |   -1.72188   .6926729    -2.49   0.013    -3.082162   -.3615977
        L5event_n |  -1.446575   .4975848    -2.91   0.004    -2.423741   -.4694103
        L6event_n |  -2.583468   .6621012    -3.90   0.000    -3.883713   -1.283223
        L7event_n |   .3487398   .7184071     0.49   0.628     -1.06208    1.759559
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   2.221558   1.338906     1.66   0.098    -.4078078    4.850923
         ew_biodt |   .3849681    .029753    12.94   0.000     .3265386    .4433976
        ew_dtmihi |  -.2305046   .0602013    -3.83   0.000    -.3487289   -.1122804
         ew_ledig |   .2014227    .080152     2.51   0.012     .0440189    .3588265
       ew_married |   .1945215   .0797599     2.44   0.015     .0378876    .3511554
        wb_anteil |  -.2415428   .0224423   -10.76   0.000    -.2856152   -.1974703
          wb_ausl |  -.0707356   .0149549    -4.73   0.000    -.1001044   -.0413669
         wb_18t24 |  -.0238847   .0275335    -0.87   0.386    -.0779554     .030186
         wb_25t34 |   .0598064   .0198031     3.02   0.003     .0209168     .098696
         wb_35t44 |  -.0068587   .0250679    -0.27   0.784    -.0560875      .04237
         wb_45t59 |  -.0342099   .0209381    -1.63   0.103    -.0753284    .0069087
          avg_dur |   .0456903   .0234304     1.95   0.052    -.0003228    .0917034
          hh_kids |  -.0574516   .0419726    -1.37   0.172    -.1398781    .0249748
mpreis_flats_rent |  -.0222702   .0235647    -0.95   0.345     -.068547    .0240066
            _cons |  -9.029568   11.38937    -0.79   0.428    -31.39621    13.33707
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      53.79
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9906
                                                  Adj R-squared   =     0.9884
                                                  Within R-sq.    =     0.4535
Number of clusters (sb_new)  =        618         Root MSE        =     1.6071

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1191316   .3136142    -0.38   0.704    -.7350122    .4967491
          F6event |   .2381386   .2653364     0.90   0.370    -.2829334    .7592106
          F5event |  -.2699391   .2574971    -1.05   0.295    -.7756161     .235738
          F4event |  -.2575768   .1705136    -1.51   0.131    -.5924342    .0772807
          F3event |   -.092262   .1615473    -0.57   0.568    -.4095112    .2249872
          F2event |  -.0389863   .1356075    -0.29   0.774    -.3052944    .2273219
          L0event |  -.4768166   .1647058    -2.89   0.004    -.8002685   -.1533648
          L1event |  -.0445109   .2041524    -0.22   0.827    -.4454286    .3564068
          L2event |   .3263285   .2180894     1.50   0.135     -.101959     .754616
          L3event |   .1514659   .2326207     0.65   0.515    -.3053583    .6082901
          L4event |  -.2152087   .8453197    -0.25   0.799    -1.875261    1.444844
          L5event |   .5390461    .648697     0.83   0.406    -.7348756    1.812968
          L6event |   .3002638    .698381     0.43   0.667    -1.071228    1.671756
          L7event |   .0015516   .8362464     0.00   0.999    -1.640683    1.643786
        F7event_n |   .3320689    .311216     1.07   0.286     -.279102    .9432399
        F6event_n |    .372637   .2387614     1.56   0.119    -.0962466    .8415206
        F5event_n |   .3148838   .2108998     1.49   0.136    -.0992846    .7290521
        F4event_n |   .2175065   .1661016     1.31   0.191    -.1086866    .5436995
        F3event_n |   .0146164   .1729095     0.08   0.933     -.324946    .3541788
        F2event_n |   .2733503    .138277     1.98   0.049     .0017997    .5449009
        L0event_n |  -.6532608   .1509115    -4.33   0.000    -.9496233   -.3568984
        L1event_n |  -.7694303   .1824347    -4.22   0.000    -1.127699   -.4111621
        L2event_n |  -.7506265   .1880983    -3.99   0.000    -1.120017   -.3812361
        L3event_n |  -.4120198   .2258488    -1.82   0.069    -.8555453    .0315057
        L4event_n |   -2.11299   .6475754    -3.26   0.001    -3.384709   -.8412713
        L5event_n |   -2.16526   .6176964    -3.51   0.000    -3.378302   -.9522179
        L6event_n |  -2.852991   1.044211    -2.73   0.006     -4.90363   -.8023519
        L7event_n |  -3.523349   .8595186    -4.10   0.000    -5.211286   -1.835412
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   1.193447   1.021464     1.17   0.243    -.8125195    3.199414
         ew_biodt |   .7541607   .0309397    24.38   0.000     .6934008    .8149207
        ew_dtmihi |  -.1672013   .0526872    -3.17   0.002    -.2706694   -.0637333
         ew_ledig |    .396456   .0686828     5.77   0.000     .2615756    .5313364
       ew_married |   .5997211   .0678317     8.84   0.000     .4665122      .73293
        wb_anteil |  -.5293117   .0237378   -22.30   0.000    -.5759284   -.4826951
          wb_ausl |  -.0567102   .0177153    -3.20   0.001    -.0914999   -.0219206
         wb_18t24 |   -.037403   .0248435    -1.51   0.133     -.086191    .0113851
         wb_25t34 |   .0014033   .0170654     0.08   0.934    -.0321099    .0349166
         wb_35t44 |   .0081806   .0206624     0.40   0.692    -.0323966    .0487578
         wb_45t59 |  -.0120232    .019844    -0.61   0.545    -.0509931    .0269468
          avg_dur |   .0144101   .0213425     0.68   0.500    -.0275026    .0563228
          hh_kids |  -.0988799   .0348011    -2.84   0.005    -.1672229   -.0305369
mpreis_flats_rent |   .0013977    .023491     0.06   0.953    -.0447344    .0475297
            _cons |   5.537037   9.648412     0.57   0.566    -13.41067    24.48475
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      13.53
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9723
                                                  Adj R-squared   =     0.9661
                                                  Within R-sq.    =     0.1790
Number of clusters (sb_new)  =        618         Root MSE        =     1.6908

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1900885   .3587676    -0.53   0.596    -.8946421    .5144651
          F6event |   .0610647   .3246539     0.19   0.851    -.5764959    .6986253
          F5event |   .2331224   .2532377     0.92   0.358    -.2641898    .7304347
          F4event |  -.0002339   .1736677    -0.00   0.999    -.3412853    .3408174
          F3event |  -.0768287    .170613    -0.45   0.653    -.4118813    .2582239
          F2event |   .0069271   .1218437     0.06   0.955    -.2323516    .2462058
          L0event |  -1.041722   .2369053    -4.40   0.000    -1.506961   -.5764839
          L1event |   -.945757   .2317256    -4.08   0.000    -1.400824   -.4906904
          L2event |   -.771708   .2536951    -3.04   0.002    -1.269919   -.2734974
          L3event |  -.3158849    .268186    -1.18   0.239    -.8425528    .2107831
          L4event |  -1.005668    .481575    -2.09   0.037    -1.951393    -.059943
          L5event |  -.7624793   .6401063    -1.19   0.234    -2.019531    .4945719
          L6event |   .8616512   .7025907     1.23   0.221    -.5181078     2.24141
          L7event |   .7830952   1.177101     0.67   0.506    -1.528515    3.094706
        F7event_n |   -.453804   .3770799    -1.20   0.229     -1.19432    .2867116
        F6event_n |  -.2967334   .1587203    -1.87   0.062    -.6084309    .0149642
        F5event_n |  -.2822764   .1877974    -1.50   0.133    -.6510759    .0865231
        F4event_n |  -.3069397   .1258323    -2.44   0.015    -.5540513   -.0598281
        F3event_n |  -.1197936   .1167189    -1.03   0.305     -.349008    .1094209
        F2event_n |  -.2086198   .1201164    -1.74   0.083    -.4445064    .0272668
        L0event_n |   .3267946   .1765665     1.85   0.065    -.0199495    .6735387
        L1event_n |   .5873726   .1872847     3.14   0.002     .2195799    .9551653
        L2event_n |   .4458256   .2236917     1.99   0.047     .0065361     .885115
        L3event_n |   .2553176   .2022431     1.26   0.207    -.1418507    .6524859
        L4event_n |   .4323708   .2671374     1.62   0.106    -.0922379    .9569795
        L5event_n |   .6194385   .3470213     1.79   0.075    -.0620477    1.300925
        L6event_n |   .1553448   1.293787     0.12   0.904    -2.385414    2.696104
        L7event_n |   .6503246   1.293878     0.50   0.615    -1.890614    3.191264
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |  -.9336964   .9662299    -0.97   0.334    -2.831194    .9638017
         ew_biodt |   .3690932   .0280381    13.16   0.000     .3140316    .4241548
        ew_dtmihi |   .0582786   .0521094     1.12   0.264    -.0440547    .1606119
         ew_ledig |    .215856   .0578202     3.73   0.000     .1023078    .3294043
       ew_married |   .4304356   .0593876     7.25   0.000     .3138092    .5470619
        wb_anteil |  -.2858504   .0203947   -14.02   0.000    -.3259019    -.245799
          wb_ausl |   .0114484   .0163637     0.70   0.484    -.0206869    .0435837
         wb_18t24 |  -.0169203   .0282075    -0.60   0.549    -.0723146    .0384741
         wb_25t34 |  -.0663578   .0195531    -3.39   0.001    -.1047564   -.0279592
         wb_35t44 |   .0060571   .0230349     0.26   0.793    -.0391792    .0512934
         wb_45t59 |    .019755   .0219566     0.90   0.369    -.0233637    .0628737
          avg_dur |   -.025275   .0213809    -1.18   0.238    -.0672631    .0167131
          hh_kids |  -.0397636   .0398074    -1.00   0.318    -.1179381    .0384109
mpreis_flats_rent |   .0294048   .0248449     1.18   0.237     -.019386    .0781956
            _cons |   11.99199   9.019244     1.33   0.184    -5.720146    29.70413
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      13.85
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9618
                                                  Adj R-squared   =     0.9532
                                                  Within R-sq.    =     0.2078
Number of clusters (sb_new)  =        618         Root MSE        =     1.6838

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0841129   .3325891     0.25   0.800     -.569031    .7372568
          F6event |   .2404512    .263311     0.91   0.362    -.2766433    .7575457
          F5event |  -.4876379   .2583785    -1.89   0.060    -.9950458      .01977
          F4event |  -.2176419    .159865    -1.36   0.174    -.5315874    .0963036
          F3event |   .0212811   .1516332     0.14   0.888    -.2764987     .319061
          F2event |  -.0628894   .1235417    -0.51   0.611    -.3055025    .1797238
          L0event |   .6207224   .2210991     2.81   0.005     .1865244     1.05492
          L1event |   .9414565   .2280724     4.13   0.000     .4935641    1.389349
          L2event |   1.051644   .2660564     3.95   0.000     .5291586     1.57413
          L3event |   .4052648   .2612135     1.55   0.121    -.1077105    .9182402
          L4event |    1.11832   .7043208     1.59   0.113    -.2648368    2.501476
          L5event |   2.407509    .605337     3.98   0.000     1.218739     3.59628
          L6event |  -.1808581   .5948195    -0.30   0.761    -1.348974    .9872581
          L7event |  -.4545707   .6912905    -0.66   0.511    -1.812138    .9029968
        F7event_n |  -.0813423   .3374318    -0.24   0.810    -.7439963    .5813117
        F6event_n |  -.0169993   .1662231    -0.10   0.919     -.343431    .3094323
        F5event_n |   .2934999   .1905438     1.54   0.124    -.0806931    .6676929
        F4event_n |  -.0986054   .1613616    -0.61   0.541      -.41549    .2182792
        F3event_n |   .0632135   .1210597     0.52   0.602    -.1745255    .3009525
        F2event_n |   .0970516   .1388868     0.70   0.485    -.1756965    .3697997
        L0event_n |  -.0916682   .1702661    -0.54   0.591    -.4260396    .2427032
        L1event_n |  -.4137538   .1831287    -2.26   0.024    -.7733848   -.0541228
        L2event_n |  -.0933525   .1923708    -0.49   0.628    -.4711334    .2844284
        L3event_n |  -.0526835   .2105869    -0.25   0.803    -.4662375    .3608705
        L4event_n |   .4436837   .4260344     1.04   0.298    -.3929696    1.280337
        L5event_n |  -.2181271   .4326012    -0.50   0.614    -1.067676    .6314222
        L6event_n |   3.518369   .7565464     4.65   0.000     2.032651    5.004088
        L7event_n |    1.55393   1.774076     0.88   0.381     -1.93003    5.037889
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   2.501879   1.369822     1.83   0.068    -.1881998    5.191958
         ew_biodt |   .3905422   .0299741    13.03   0.000     .3316786    .4494058
        ew_dtmihi |  -.2299817    .060709    -3.79   0.000    -.3492029   -.1107605
         ew_ledig |     .20953   .0810868     2.58   0.010     .0502905    .3687696
       ew_married |   .2052807   .0804102     2.55   0.011     .0473698    .3631917
        wb_anteil |  -.2441019   .0229367   -10.64   0.000    -.2891454   -.1990584
          wb_ausl |  -.0684906   .0145297    -4.71   0.000    -.0970243   -.0399569
         wb_18t24 |  -.0243758   .0275112    -0.89   0.376    -.0784028    .0296512
         wb_25t34 |   .0508026   .0196569     2.58   0.010     .0122001    .0894052
         wb_35t44 |  -.0107427   .0248151    -0.43   0.665    -.0594751    .0379896
         wb_45t59 |   -.040725   .0205854    -1.98   0.048    -.0811509   -.0002991
          avg_dur |   .0456374   .0235367     1.94   0.053    -.0005844    .0918592
          hh_kids |   -.065818   .0418413    -1.57   0.116    -.1479866    .0163505
mpreis_flats_rent |   -.014455   .0234992    -0.62   0.539    -.0606031    .0316931
            _cons |  -11.77313   11.46663    -1.03   0.305    -34.29148    10.74522
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      33.63
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4451
Number of clusters (sb_new)  =        618         Root MSE        =     1.6194

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1059756   .3249184    -0.33   0.744    -.7440557    .5321044
          F6event |   .3015152   .2744805     1.10   0.272     -.237514    .8405444
          F5event |  -.2545151   .2612421    -0.97   0.330    -.7675465    .2585163
          F4event |  -.2178755   .1675719    -1.30   0.194    -.5469558    .1112049
          F3event |  -.0555478   .1601087    -0.35   0.729    -.3699719    .2588764
          F2event |  -.0559618   .1330598    -0.42   0.674    -.3172667    .2053431
          L0event |  -.4209997   .1632377    -2.58   0.010    -.7415685   -.1004309
          L1event |  -.0043002    .203343    -0.02   0.983    -.4036284     .395028
          L2event |   .2799366   .2223328     1.26   0.208    -.1566842    .7165573
          L3event |   .0893804   .2330807     0.38   0.702    -.3683473     .547108
          L4event |   .1126526   .7308905     0.15   0.878    -1.322682    1.547987
          L5event |    1.64503   .8098136     2.03   0.043     .0547051    3.235355
          L6event |   .6807914   .5532818     1.23   0.219    -.4057525    1.767335
          L7event |   .3285258   1.077622     0.30   0.761    -1.787727    2.444778
        F7event_n |  -.5351451    .340017    -1.57   0.116    -1.202876    .1325858
        F6event_n |  -.3137332   .1269738    -2.47   0.014    -.5630864     -.06438
        F5event_n |   .0112231    .147012     0.08   0.939    -.2774815    .2999276
        F4event_n |  -.4055453   .1955953    -2.07   0.039    -.7896586   -.0214321
        F3event_n |    -.05658   .1170941    -0.48   0.629    -.2865314    .1733713
        F2event_n |  -.1115682   .1256022    -0.89   0.375    -.3582278    .1350915
        L0event_n |   .2351263    .123362     1.91   0.057     -.007134    .4773867
        L1event_n |    .173619   .1375021     1.26   0.207    -.0964098    .4436478
        L2event_n |    .352473   .1558986     2.26   0.024     .0463168    .6586293
        L3event_n |   .2026339   .1682712     1.20   0.229    -.1278198    .5330876
        L4event_n |   .8760543   .3131091     2.80   0.005     .2611656    1.490943
        L5event_n |   .4013107   .3142688     1.28   0.202    -.2158555    1.018477
        L6event_n |    3.67371   1.072999     3.42   0.001     1.566537    5.780883
        L7event_n |   2.204253    1.11623     1.97   0.049     .0121827    4.396323
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   1.568183   1.052357     1.49   0.137    -.4984541    3.634819
         ew_biodt |   .7596354   .0311846    24.36   0.000     .6983945    .8208762
        ew_dtmihi |   -.171703   .0526113    -3.26   0.001    -.2750219    -.068384
         ew_ledig |   .4253862    .068885     6.18   0.000     .2901088    .5606636
       ew_married |   .6357164   .0678781     9.37   0.000     .5024162    .7690165
        wb_anteil |  -.5299523   .0238256   -22.24   0.000    -.5767414   -.4831633
          wb_ausl |  -.0570422   .0179791    -3.17   0.002    -.0923499   -.0217345
         wb_18t24 |  -.0412962   .0248085    -1.66   0.097    -.0900156    .0074232
         wb_25t34 |  -.0155552   .0166149    -0.94   0.350    -.0481839    .0170735
         wb_35t44 |  -.0046856   .0209003    -0.22   0.823      -.04573    .0363588
         wb_45t59 |    -.02097   .0198878    -1.05   0.292    -.0600259    .0180859
          avg_dur |   .0203624   .0221426     0.92   0.358    -.0231215    .0638463
          hh_kids |  -.1055817   .0346071    -3.05   0.002    -.1735438   -.0376197
mpreis_flats_rent |   .0149498   .0232648     0.64   0.521    -.0307379    .0606375
            _cons |     .21885    9.92708     0.02   0.982    -19.27611    19.71381
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      13.79
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9722
                                                  Adj R-squared   =     0.9659
                                                  Within R-sq.    =     0.1759
Number of clusters (sb_new)  =        618         Root MSE        =     1.6941

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   -.180454   .3407207    -0.53   0.597    -.8495668    .4886589
          F6event |   .0267999   .3221207     0.08   0.934    -.6057859    .6593858
          F5event |   .2533436   .2588735     0.98   0.328    -.2550364    .7617235
          F4event |   .0090714    .175104     0.05   0.959    -.3348006    .3529434
          F3event |  -.0553295   .1718277    -0.32   0.748    -.3927675    .2821084
          F2event |    .006031   .1238851     0.05   0.961    -.2372566    .2493186
          L0event |  -.9730121   .2382542    -4.08   0.000      -1.4409   -.5051247
          L1event |  -.8599637   .2355216    -3.65   0.000    -1.322485   -.3974426
          L2event |  -.7424583   .2718391    -2.73   0.006      -1.2763   -.2086163
          L3event |  -.3301569   .2771917    -1.19   0.234    -.8745105    .2141967
          L4event |  -.6471576    .458128    -1.41   0.158    -1.546837    .2525216
          L5event |  -.1349751   .6106775    -0.22   0.825    -1.334234    1.064283
          L6event |   .3034708   .5529008     0.55   0.583    -.7823247    1.389266
          L7event |   1.039194    .777402     1.34   0.182    -.4874806    2.565869
        F7event_n |   .7404123   .2951031     2.51   0.012     .1608839    1.319941
        F6event_n |   .1173815   .3106741     0.38   0.706    -.4927253    .7274884
        F5event_n |   .0574165   .2579851     0.22   0.824    -.4492187    .5640518
        F4event_n |  -.2394951   .1625863    -1.47   0.141    -.5587847    .0797946
        F3event_n |  -.1803557   .1773602    -1.02   0.310    -.5286586    .1679472
        F2event_n |  -.1036881   .1463065    -0.71   0.479    -.3910072     .183631
        L0event_n |   .3778658   .2643776     1.43   0.153    -.1413233    .8970548
        L1event_n |   .2605075   .2134104     1.22   0.223    -.1585913    .6796062
        L2event_n |   .1660634   .3557016     0.47   0.641    -.5324691    .8645959
        L3event_n |  -.0617405   .3362234    -0.18   0.854    -.7220216    .5985405
        L4event_n |   .6937606   .4984113     1.39   0.164    -.2850276    1.672549
        L5event_n |   .6747957   .6102835     1.11   0.269     -.523689     1.87328
        L6event_n |  -.9308037   .6354534    -1.46   0.143    -2.178717      .31711
        L7event_n |   .3629733   1.247489     0.29   0.771    -2.086865    2.812812
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |  -1.221001   .9404128    -1.30   0.195    -3.067799    .6257965
         ew_biodt |   .3728164   .0281848    13.23   0.000     .3174666    .4281663
        ew_dtmihi |   .0530022   .0520758     1.02   0.309    -.0492651    .1552696
         ew_ledig |   .2108207   .0579763     3.64   0.000     .0969659    .3246756
       ew_married |   .4253233   .0590884     7.20   0.000     .3092846     .541362
        wb_anteil |  -.2933733    .020433   -14.36   0.000    -.3334999   -.2532467
          wb_ausl |   .0157876   .0158307     1.00   0.319    -.0153009    .0468761
         wb_18t24 |   -.020767   .0299688    -0.69   0.489    -.0796202    .0380861
         wb_25t34 |  -.0690185   .0190472    -3.62   0.000    -.1064238   -.0316133
         wb_35t44 |   .0076083   .0232878     0.33   0.744    -.0381246    .0533413
         wb_45t59 |   .0122609   .0219509     0.56   0.577    -.0308466    .0553685
          avg_dur |  -.0308712    .020643    -1.50   0.135    -.0714103     .009668
          hh_kids |  -.0313047   .0402356    -0.78   0.437      -.11032    .0477106
mpreis_flats_rent |   .0318701   .0261594     1.22   0.224    -.0195022    .0832424
            _cons |   15.15653   8.808898     1.72   0.086    -2.142529    32.45558
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      13.53
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9619
                                                  Adj R-squared   =     0.9533
                                                  Within R-sq.    =     0.2100
Number of clusters (sb_new)  =        618         Root MSE        =     1.6815

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0954779   .3264276     0.29   0.770    -.5455659    .7365218
          F6event |   .2362105   .2640228     0.89   0.371    -.2822818    .7547027
          F5event |   -.482305   .2643811    -1.82   0.069    -1.001501     .036891
          F4event |  -.2339308   .1625936    -1.44   0.151    -.5532348    .0853731
          F3event |   .0128317   .1547964     0.08   0.934      -.29116    .3168234
          F2event |  -.0430337   .1242704    -0.35   0.729    -.2870779    .2010104
          L0event |   .5877365   .2198272     2.67   0.008     .1560363    1.019437
          L1event |   .9022459   .2287669     3.94   0.000     .4529897    1.351502
          L2event |   1.042353   .2660888     3.92   0.000     .5198031    1.564902
          L3event |   .3368199   .2714718     1.24   0.215    -.1963008    .8699407
          L4event |   1.295297   .6437055     2.01   0.045     .0311782    2.559417
          L5event |   2.352328   .6478742     3.63   0.000     1.080022    3.624634
          L6event |   .2865163   1.031784     0.28   0.781    -1.739718     2.31275
          L7event |   .4966588   .5203733     0.95   0.340    -.5252587    1.518576
        F7event_n |  -.3504935   .3020838    -1.16   0.246    -.9437307    .2427436
        F6event_n |  -.0848727   .2068344    -0.41   0.682    -.4910575     .321312
        F5event_n |   .0606676   .2079544     0.29   0.771    -.3477166    .4690519
        F4event_n |   .1599684   .1790436     0.89   0.372    -.1916403    .5115771
        F3event_n |   .2274898   .2044513     1.11   0.266    -.1740149    .6289946
        F2event_n |   .2596024   .1449337     1.79   0.074    -.0250208    .5442255
        L0event_n |  -.3483652   .2054561    -1.70   0.090    -.7518433    .0551129
        L1event_n |  -.1099159   .1977092    -0.56   0.578    -.4981805    .2783486
        L2event_n |  -.1148772   .2766974    -0.42   0.678    -.6582601    .4285056
        L3event_n |  -.3276979   .3421085    -0.96   0.338     -.999536    .3441403
        L4event_n |  -.7158064    .535891    -1.34   0.182    -1.768198     .336585
        L5event_n |  -.2842194     .55934    -0.51   0.612     -1.38266    .8142217
        L6event_n |    .747195   .7888179     0.95   0.344    -.8018984    2.296288
        L7event_n |   1.240762   .5353268     2.32   0.021     .1894785    2.292045
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   2.869722   1.412958     2.03   0.043      .094932    5.644513
         ew_biodt |   .3897425   .0300974    12.95   0.000     .3306368    .4488482
        ew_dtmihi |  -.2208206   .0613336    -3.60   0.000    -.3412684   -.1003728
         ew_ledig |   .2132628   .0799591     2.67   0.008     .0562378    .3702877
       ew_married |   .2138993   .0796415     2.69   0.007      .057498    .3703006
        wb_anteil |  -.2390679   .0222703   -10.73   0.000    -.2828027   -.1953332
          wb_ausl |  -.0699276   .0143545    -4.87   0.000    -.0981172    -.041738
         wb_18t24 |  -.0226005   .0278386    -0.81   0.417    -.0772703    .0320693
         wb_25t34 |    .050032   .0192606     2.60   0.010     .0122077    .0878563
         wb_35t44 |  -.0103261   .0249903    -0.41   0.680    -.0594025    .0387502
         wb_45t59 |  -.0363213   .0205335    -1.77   0.077    -.0766454    .0040028
          avg_dur |   .0478038   .0227432     2.10   0.036     .0031403    .0924674
          hh_kids |  -.0835753   .0450171    -1.86   0.064    -.1719806      .00483
mpreis_flats_rent |  -.0193432   .0238056    -0.81   0.417     -.066093    .0274066
            _cons |  -15.31634   11.77544    -1.30   0.194    -38.44113    7.808459
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      31.78
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4411
Number of clusters (sb_new)  =        618         Root MSE        =     1.6253

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0849762   .3248731    -0.26   0.794    -.7229673    .5530149
          F6event |   .2630096   .2761509     0.95   0.341       -.2793    .8053193
          F5event |  -.2289611   .2643564    -0.87   0.387    -.7481085    .2901863
          F4event |  -.2248591   .1710408    -1.31   0.189    -.5607518    .1110336
          F3event |  -.0424981   .1588078    -0.27   0.789    -.3543674    .2693712
          F2event |  -.0370023   .1319214    -0.28   0.779    -.2960717    .2220671
          L0event |  -.3852753   .1649489    -2.34   0.020    -.7092047   -.0613459
          L1event |   .0422825   .2027565     0.21   0.835     -.355894    .4404591
          L2event |   .2998945   .2311089     1.30   0.195    -.1539609      .75375
          L3event |   .0066634   .2384906     0.03   0.978    -.4616883    .4750151
          L4event |   .6481402   .6967638     0.93   0.353    -.7201758    2.016456
          L5event |   2.217351   1.009091     2.20   0.028     .2356818    4.199021
          L6event |   .5899863   1.139766     0.52   0.605    -1.648305    2.828278
          L7event |   1.535852   .7812552     1.97   0.050     .0016108    3.070094
        F7event_n |    .389919   .2546891     1.53   0.126    -.1102437    .8900817
        F6event_n |   .0325088   .2365332     0.14   0.891    -.4319989    .4970165
        F5event_n |   .1180839   .2172753     0.54   0.587    -.3086048    .5447727
        F4event_n |  -.0795266   .1630591    -0.49   0.626    -.3997448    .2406916
        F3event_n |   .0471341   .1527825     0.31   0.758    -.2529026    .3471709
        F2event_n |   .1559143   .1115074     1.40   0.163    -.0630658    .3748943
        L0event_n |   .0295006   .1568547     0.19   0.851    -.2785332    .3375344
        L1event_n |   .1505915   .1427568     1.05   0.292    -.1297565    .4309396
        L2event_n |   .0511865   .2373705     0.22   0.829    -.4149656    .5173386
        L3event_n |  -.3894384   .2582978    -1.51   0.132    -.8966879     .117811
        L4event_n |  -.0220458   .4770124    -0.05   0.963    -.9588105    .9147188
        L5event_n |   .3905748   .6991571     0.56   0.577    -.9824412    1.763591
        L6event_n |  -.1836074   .7966534    -0.23   0.818    -1.748088    1.380874
        L7event_n |   1.603733   1.014727     1.58   0.115    -.3890042     3.59647
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   1.648721    1.07367     1.54   0.125    -.4597691    3.757211
         ew_biodt |    .762559   .0316607    24.09   0.000     .7003833    .8247347
        ew_dtmihi |  -.1678182   .0531119    -3.16   0.002    -.2721202   -.0635163
         ew_ledig |   .4240837   .0700966     6.05   0.000     .2864269    .5617404
       ew_married |   .6392227   .0691113     9.25   0.000     .5035008    .7749446
        wb_anteil |  -.5324412   .0240447   -22.14   0.000    -.5796606   -.4852219
          wb_ausl |  -.0541399   .0176362    -3.07   0.002    -.0887742   -.0195057
         wb_18t24 |  -.0433675   .0258305    -1.68   0.094    -.0940938    .0073588
         wb_25t34 |  -.0189865   .0167413    -1.13   0.257    -.0518633    .0138902
         wb_35t44 |  -.0027178   .0208677    -0.13   0.896    -.0436981    .0382625
         wb_45t59 |  -.0240604   .0197457    -1.22   0.223    -.0628372    .0147165
          avg_dur |   .0169327    .022077     0.77   0.443    -.0264224    .0602878
          hh_kids |  -.1148801    .037526    -3.06   0.002    -.1885744   -.0411859
mpreis_flats_rent |   .0125269   .0241475     0.52   0.604    -.0348943    .0599481
            _cons |  -.1598231   9.984369    -0.02   0.987    -19.76729    19.44764
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      13.83
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9657
                                                  Within R-sq.    =     0.1700
Number of clusters (sb_new)  =        618         Root MSE        =     1.7001

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1508549   .3536981    -0.43   0.670    -.8454529    .5437431
          F6event |   .0542902   .3212932     0.17   0.866    -.5766706    .6852509
          F5event |   .2733154   .2526861     1.08   0.280    -.2229137    .7695445
          F4event |   .0157165   .1733466     0.09   0.928    -.3247043    .3561373
          F3event |  -.0567567   .1732078    -0.33   0.743    -.3969051    .2833916
          F2event |   .0170116   .1240922     0.14   0.891    -.2266826    .2607059
          L0event |  -.9669165   .2313094    -4.18   0.000    -1.421166   -.5126673
          L1event |  -.8857899    .232332    -3.81   0.000    -1.342047   -.4295325
          L2event |  -.7398716   .2600511    -2.85   0.005    -1.250564    -.229179
          L3event |  -.2956753    .267321    -1.11   0.269    -.8206446    .2292941
          L4event |  -.9037075   .4616996    -1.96   0.051    -1.810401    .0029857
          L5event |  -.5334916   .8193408    -0.65   0.515    -2.142526    1.075543
          L6event |   .6157895   .5476528     1.12   0.261    -.4596999    1.691279
          L7event |   1.066064   1.003511     1.06   0.288    -.9046467    3.036774
        F7event_n |  -.0295015   .3926944    -0.08   0.940    -.8006812    .7416781
        F6event_n |  -.0854066   .3425723    -0.25   0.803    -.7581556    .5873424
        F5event_n |  -.0684928   .2326068    -0.29   0.769    -.5252899    .3883043
        F4event_n |   .0502975   .1447052     0.35   0.728     -.233877    .3344719
        F3event_n |   .0645413   .1209124     0.53   0.594    -.1729084     .301991
        F2event_n |  -.0151612   .1085099    -0.14   0.889    -.2282547    .1979323
        L0event_n |  -.1709389   .2601831    -0.66   0.511    -.6818907     .340013
        L1event_n |  -.0211206   .2616152    -0.08   0.936    -.5348848    .4926436
        L2event_n |  -.0461809   .2358325    -0.20   0.845    -.5093127    .4169509
        L3event_n |   .0692134   .2733017     0.25   0.800    -.4675008    .6059276
        L4event_n |   .0214371   .9492657     0.02   0.982    -1.842746    1.885621
        L5event_n |  -.1162563   1.446034    -0.08   0.936    -2.956002     2.72349
        L6event_n |   2.117175   1.121602     1.89   0.060    -.0854454    4.319796
        L7event_n |  -.5796369   1.810217    -0.32   0.749     -4.13457    2.975296
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |  -.8193246    .951344    -0.86   0.389    -2.687589     1.04894
         ew_biodt |     .37408   .0281987    13.27   0.000      .318703     .429457
        ew_dtmihi |   .0621696   .0516193     1.20   0.229    -.0392013    .1635405
         ew_ledig |   .2118658   .0572869     3.70   0.000     .0993649    .3243667
       ew_married |    .431172   .0591147     7.29   0.000     .3150815    .5472625
        wb_anteil |  -.2904401   .0202912   -14.31   0.000    -.3302883   -.2505918
          wb_ausl |   .0152008   .0162502     0.94   0.350    -.0167117    .0471133
         wb_18t24 |  -.0161341   .0297718    -0.54   0.588    -.0746005    .0423323
         wb_25t34 |  -.0720688    .019491    -3.70   0.000    -.1103456    -.033792
         wb_35t44 |   .0062614   .0231889     0.27   0.787    -.0392772    .0518001
         wb_45t59 |   .0145288   .0222363     0.65   0.514    -.0291393    .0581969
          avg_dur |  -.0285924   .0211068    -1.35   0.176    -.0700422    .0128574
          hh_kids |  -.0485991   .0410872    -1.18   0.237    -.1292868    .0320886
mpreis_flats_rent |   .0284282    .025796     1.10   0.271    -.0222305    .0790868
            _cons |   11.66191   9.039137     1.29   0.197     -6.08929    29.41312
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      12.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9617
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2071
Number of clusters (sb_new)  =        618         Root MSE        =     1.6845

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0729558   .3233509     0.23   0.822     -.562046    .7079577
          F6event |   .1990994   .2588524     0.77   0.442    -.3092391    .7074378
          F5event |  -.4674483   .2532918    -1.85   0.065    -.9648668    .0299702
          F4event |   -.241683   .1577507    -1.53   0.126    -.5514765    .0681105
          F3event |   .0029359   .1536469     0.02   0.985    -.2987983    .3046702
          F2event |  -.0470324   .1233528    -0.38   0.703    -.2892746    .1952098
          L0event |   .5594715   .2144626     2.61   0.009     .1383063    .9806367
          L1event |   .9043057   .2263167     4.00   0.000     .4598612     1.34875
          L2event |   1.032805   .2648896     3.90   0.000     .5126103    1.552999
          L3event |    .407347   .2593031     1.57   0.117    -.1018766    .9165707
          L4event |   1.470122   .7669853     1.92   0.056    -.0360965     2.97634
          L5event |   2.459859   .7180745     3.43   0.001     1.049693    3.870026
          L6event |   .1493915   .7223199     0.21   0.836    -1.269112    1.567895
          L7event |   .0794647   .5044141     0.16   0.875    -.9111119    1.070041
        F7event_n |  -.2874962   .2754531    -1.04   0.297    -.8284355    .2534431
        F6event_n |   -.117081   .2452752    -0.48   0.633    -.5987565    .3645944
        F5event_n |  -.2445298   .2428435    -1.01   0.314    -.7214297    .2323702
        F4event_n |  -.1018938   .1442979    -0.71   0.480    -.3852682    .1814807
        F3event_n |  -.0874883   .1210173    -0.72   0.470     -.325144    .1501674
        F2event_n |  -.1243053     .09371    -1.33   0.185    -.3083345    .0597239
        L0event_n |   .2939173   .2061478     1.43   0.154    -.1109191    .6987536
        L1event_n |   .0192411   .2091168     0.09   0.927    -.3914258     .429908
        L2event_n |   .0277559   .2084473     0.13   0.894    -.3815964    .4371082
        L3event_n |   .0662554   .3032876     0.22   0.827    -.5293457    .6618566
        L4event_n |   .3664142   1.202967     0.30   0.761    -1.995992     2.72882
        L5event_n |  -.0731026   1.167655    -0.06   0.950    -2.366163    2.219958
        L6event_n |  -2.284223   .9663228    -2.36   0.018    -4.181903   -.3865423
        L7event_n |  -2.448343   .8375774    -2.92   0.004    -4.093192   -.8034953
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   2.556726   1.333563     1.92   0.056    -.0621457    5.175599
         ew_biodt |   .3879103   .0295286    13.14   0.000     .3299215    .4458991
        ew_dtmihi |  -.2300958    .059589    -3.86   0.000    -.3471176    -.113074
         ew_ledig |   .2124179   .0792043     2.68   0.008     .0568751    .3679607
       ew_married |   .2050265   .0789292     2.60   0.010     .0500239     .360029
        wb_anteil |  -.2401255   .0222197   -10.81   0.000    -.2837608   -.1964902
          wb_ausl |  -.0685668   .0147482    -4.65   0.000    -.0975296   -.0396039
         wb_18t24 |  -.0297363   .0275819    -1.08   0.281    -.0839021    .0244296
         wb_25t34 |   .0514005   .0194855     2.64   0.009     .0131345    .0896665
         wb_35t44 |  -.0090233   .0251644    -0.36   0.720    -.0584416    .0403951
         wb_45t59 |  -.0396897   .0204644    -1.94   0.053     -.079878    .0004985
          avg_dur |   .0449153   .0233793     1.92   0.055    -.0009974     .090828
          hh_kids |  -.0680793   .0420594    -1.62   0.106    -.1506762    .0145175
mpreis_flats_rent |  -.0162175   .0240194    -0.68   0.500    -.0633872    .0309521
            _cons |  -12.36604   11.26992    -1.10   0.273    -34.49809    9.766021
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      30.37
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4409
Number of clusters (sb_new)  =        618         Root MSE        =     1.6255

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0778992   .3153553    -0.25   0.805    -.6971992    .5414007
          F6event |   .2533887   .2761877     0.92   0.359    -.2889932    .7957706
          F5event |  -.1941326   .2581625    -0.75   0.452    -.7011163    .3128512
          F4event |  -.2259662   .1702722    -1.33   0.185    -.5603496    .1084171
          F3event |  -.0538211     .15996    -0.34   0.737    -.3679531     .260311
          F2event |  -.0300204   .1334456    -0.22   0.822    -.2920829    .2320422
          L0event |  -.4074447   .1661781    -2.45   0.014     -.733788   -.0811014
          L1event |   .0185161   .2049346     0.09   0.928    -.3839377      .42097
          L2event |   .2929334   .2283496     1.28   0.200    -.1555033    .7413701
          L3event |    .111672   .2322526     0.48   0.631    -.3444294    .5677734
          L4event |   .5664161   .7712874     0.73   0.463    -.9482506    2.081083
          L5event |   1.926366   1.334767     1.44   0.149    -.6948708    4.547604
          L6event |   .7651797   .8638213     0.89   0.376    -.9312066    2.461566
          L7event |   1.145529    .854039     1.34   0.180    -.5316467    2.822705
        F7event_n |  -.3169981   .3470135    -0.91   0.361     -.998469    .3644727
        F6event_n |  -.2024882   .2888314    -0.70   0.484    -.7696999    .3647235
        F5event_n |  -.3130222   .2095211    -1.49   0.136    -.7244832    .0984388
        F4event_n |   -.051596   .1459701    -0.35   0.724    -.3382543    .2350624
        F3event_n |   -.022947   .1132005    -0.20   0.839     -.245252     .199358
        F2event_n |  -.1394662   .0996137    -1.40   0.162    -.3350892    .0561567
        L0event_n |   .1229785    .152895     0.80   0.422    -.1772792    .4232362
        L1event_n |  -.0018795   .1738082    -0.01   0.991    -.3432068    .3394478
        L2event_n |  -.0184243   .1565928    -0.12   0.906    -.3259438    .2890951
        L3event_n |   .1354699   .2630785     0.51   0.607    -.3811679    .6521076
        L4event_n |   .3878468   1.012434     0.38   0.702    -1.600387    2.376081
        L5event_n |  -.1893567   2.059822    -0.09   0.927    -4.234468    3.855755
        L6event_n |  -.1670483   1.141209    -0.15   0.884    -2.408173    2.074076
        L7event_n |  -3.027977   1.596242    -1.90   0.058    -6.162703    .1067501
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   1.737402   1.060191     1.64   0.102    -.3446175    3.819422
         ew_biodt |   .7619904   .0315031    24.19   0.000     .7001241    .8238567
        ew_dtmihi |   -.167926   .0523439    -3.21   0.001    -.2707199   -.0651321
         ew_ledig |   .4242839   .0698846     6.07   0.000     .2870434    .5615243
       ew_married |   .6361985   .0687637     9.25   0.000     .5011593    .7712377
        wb_anteil |  -.5305656   .0239371   -22.17   0.000    -.5775736   -.4835575
          wb_ausl |   -.053366   .0175671    -3.04   0.002    -.0878645   -.0188675
         wb_18t24 |  -.0458704   .0259364    -1.77   0.077    -.0968046    .0050639
         wb_25t34 |  -.0206683   .0166053    -1.24   0.214     -.053278    .0119414
         wb_35t44 |  -.0027618   .0209442    -0.13   0.895    -.0438923    .0383687
         wb_45t59 |  -.0251609   .0197865    -1.27   0.204    -.0640179    .0136961
          avg_dur |   .0163229   .0220259     0.74   0.459    -.0269319    .0595778
          hh_kids |  -.1166785    .035843    -3.26   0.001    -.1870675   -.0462896
mpreis_flats_rent |   .0122106   .0237695     0.51   0.608    -.0344683    .0588895
            _cons |  -.7041351    9.97138    -0.07   0.944    -20.28609    18.87782
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      13.07
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9724
                                                  Adj R-squared   =     0.9661
                                                  Within R-sq.    =     0.1803
Number of clusters (sb_new)  =        618         Root MSE        =     1.6895

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0932747    .355014    -0.26   0.793     -.790457    .6039076
          F6event |   .0847522   .3134098     0.27   0.787    -.5307271    .7002316
          F5event |   .2965342   .2519466     1.18   0.240    -.1982427     .791311
          F4event |   .0565008   .1748583     0.32   0.747    -.2868888    .3998903
          F3event |  -.0239732   .1729555    -0.14   0.890    -.3636261    .3156797
          F2event |   .0106947   .1216874     0.09   0.930     -.228277    .2496664
          L0event |  -.9713023   .2333643    -4.16   0.000    -1.429587   -.5130178
          L1event |   -.875188   .2328466    -3.76   0.000    -1.332456   -.4179201
          L2event |  -.6994006   .2513143    -2.78   0.006    -1.192936   -.2058655
          L3event |  -.2602405    .258857    -1.01   0.315    -.7685881    .2481071
          L4event |  -.4868855   .4855019    -1.00   0.316    -1.440322     .466551
          L5event |  -.0852162   .5418645    -0.16   0.875    -1.149339    .9789061
          L6event |   .3230143   .6010685     0.54   0.591    -.8573739    1.503402
          L7event |   1.763019   1.343056     1.31   0.190    -.8744966    4.400535
        F7event_n |   .3498078   .3779925     0.93   0.355    -.3925001    1.092116
        F6event_n |   .3571562   .3082497     1.16   0.247    -.2481897     .962502
        F5event_n |   .3841062   .2667822     1.44   0.150     -.139805    .9080175
        F4event_n |  -.0597041   .1824385    -0.33   0.744    -.4179798    .2985716
        F3event_n |   .0949484   .1887914     0.50   0.615    -.2758032       .4657
        F2event_n |   .0001487   .1378047     0.00   0.999    -.2704743    .2707718
        L0event_n |   .7421387   .2538021     2.92   0.004     .2437181    1.240559
        L1event_n |   .5474069   .1998865     2.74   0.006     .1548665    .9399473
        L2event_n |   .6945353   .2515884     2.76   0.006     .2004619    1.188609
        L3event_n |   .4441997   .2397913     1.85   0.064    -.0267065    .9151058
        L4event_n |   1.125713   .5163634     2.18   0.030     .1116703    2.139756
        L5event_n |   1.252716   .6865734     1.82   0.069    -.0955881     2.60102
        L6event_n |  -.7757984   .8230019    -0.94   0.346    -2.392023     .840426
        L7event_n |   2.111857   1.367086     1.54   0.123    -.5728497    4.796563
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |  -1.150468   .9293057    -1.24   0.216    -2.975453    .6745179
         ew_biodt |    .372472   .0281134    13.25   0.000     .3172625    .4276815
        ew_dtmihi |   .0505296   .0519897     0.97   0.331    -.0515686    .1526279
         ew_ledig |   .2206547   .0570143     3.87   0.000     .1086892    .3326202
       ew_married |   .4354973   .0586095     7.43   0.000      .320399    .5505955
        wb_anteil |  -.2919451     .02037   -14.33   0.000     -.331948   -.2519422
          wb_ausl |   .0164249   .0161437     1.02   0.309    -.0152784    .0481283
         wb_18t24 |  -.0153985   .0296792    -0.52   0.604    -.0736831     .042886
         wb_25t34 |   -.070256   .0194117    -3.62   0.000     -.108377   -.0321349
         wb_35t44 |   .0046889   .0230283     0.20   0.839    -.0405345    .0499123
         wb_45t59 |     .01131   .0221123     0.51   0.609    -.0321144    .0547345
          avg_dur |  -.0239562   .0210518    -1.14   0.256     -.065298    .0173856
          hh_kids |  -.0323894   .0396436    -0.82   0.414    -.1102421    .0454633
mpreis_flats_rent |   .0317181    .025066     1.27   0.206     -.017507    .0809432
            _cons |   13.62224   8.782258     1.55   0.121    -3.624497    30.86898
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      15.60
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9625
                                                  Adj R-squared   =     0.9540
                                                  Within R-sq.    =     0.2228
Number of clusters (sb_new)  =        618         Root MSE        =     1.6678

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0436294   .3218833    -0.14   0.892    -.6757492    .5884903
          F6event |   .1245385   .2602304     0.48   0.632    -.3865062    .6355831
          F5event |  -.5849204   .2590288    -2.26   0.024    -1.093605   -.0762355
          F4event |  -.2997295   .1599065    -1.87   0.061    -.6137565    .0142976
          F3event |  -.0309874   .1540136    -0.20   0.841    -.3334419    .2714671
          F2event |  -.0604655   .1235629    -0.49   0.625    -.3031203    .1821894
          L0event |   .5969134   .2173488     2.75   0.006     .1700803    1.023747
          L1event |   .8968731   .2258852     3.97   0.000     .4532762     1.34047
          L2event |   .9768998   .2530164     3.86   0.000     .4800221    1.473777
          L3event |   .2900326    .244445     1.19   0.236    -.1900124    .7700776
          L4event |   1.415992   .5261762     2.69   0.007     .3826788    2.449306
          L5event |    2.40283   .6108835     3.93   0.000     1.203167    3.602493
          L6event |   .7899449   .5755101     1.37   0.170    -.3402511    1.920141
          L7event |    .406845   .6236713     0.65   0.514    -.8179308    1.631621
        F7event_n |   .2498355   .2682673     0.93   0.352    -.2769923    .7766632
        F6event_n |  -.0776619    .203181    -0.38   0.702    -.4766721    .3213484
        F5event_n |   .1476667   .2305703     0.64   0.522    -.3051311    .6004645
        F4event_n |   .2021401    .188505     1.07   0.284    -.1680491    .5723294
        F3event_n |   .1200338   .1940462     0.62   0.536    -.2610373    .5011049
        F2event_n |   .3702092   .1348861     2.74   0.006     .1053176    .6351007
        L0event_n |   -.643548   .1895232    -3.40   0.001    -1.015737   -.2713592
        L1event_n |  -.6344856   .2075546    -3.06   0.002    -1.042085   -.2268865
        L2event_n |  -.8315072   .1950463    -4.26   0.000    -1.214542   -.4484721
        L3event_n |  -.9365992   .2372967    -3.95   0.000    -1.402606   -.4705922
        L4event_n |  -.7594945   .5230927    -1.45   0.147    -1.786753    .2677636
        L5event_n |  -.5916307   .6680688    -0.89   0.376    -1.903595    .7203338
        L6event_n |    1.32079   .7289532     1.81   0.070    -.1107403     2.75232
        L7event_n |   .7830798   .6435686     1.22   0.224    -.4807706     2.04693
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   2.926271   1.378068     2.12   0.034     .2199985    5.632544
         ew_biodt |   .3931311   .0293656    13.39   0.000     .3354625    .4507997
        ew_dtmihi |  -.2072287     .06044    -3.43   0.001    -.3259219   -.0885356
         ew_ledig |   .2019745    .076976     2.62   0.009     .0508078    .3531413
       ew_married |   .2032165   .0767184     2.65   0.008     .0525556    .3538774
        wb_anteil |  -.2436663   .0218823   -11.14   0.000    -.2866391   -.2006934
          wb_ausl |  -.0682278   .0144709    -4.71   0.000     -.096646   -.0398096
         wb_18t24 |  -.0288157   .0275381    -1.05   0.296    -.0828955     .025264
         wb_25t34 |   .0480063   .0191594     2.51   0.012     .0103807    .0856319
         wb_35t44 |  -.0039168   .0246325    -0.16   0.874    -.0522904    .0444568
         wb_45t59 |  -.0354178   .0203738    -1.74   0.083    -.0754282    .0045925
          avg_dur |   .0394911   .0224687     1.76   0.079    -.0046333    .0836155
          hh_kids |  -.0999369     .04383    -2.28   0.023    -.1860109   -.0138629
mpreis_flats_rent |  -.0190342    .023209    -0.82   0.412    -.0646124    .0265441
            _cons |   -14.4856   11.50347    -1.26   0.208     -37.0763    8.105113
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      33.00
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4437
Number of clusters (sb_new)  =        618         Root MSE        =     1.6215

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1369043   .3154321    -0.43   0.664    -.7563549    .4825464
          F6event |   .2092899   .2689914     0.78   0.437    -.3189597    .7375396
          F5event |  -.2883858    .255742    -1.13   0.260    -.7906161    .2138444
          F4event |  -.2432284   .1687749    -1.44   0.150    -.5746712    .0882144
          F3event |  -.0549609   .1573757    -0.35   0.727    -.3640178     .254096
          F2event |  -.0497703    .130663    -0.38   0.703    -.3063683    .2068277
          L0event |  -.3743886   .1645657    -2.28   0.023    -.6975653   -.0512119
          L1event |   .0216854   .2035011     0.11   0.915    -.3779533    .4213242
          L2event |   .2774994   .2307961     1.20   0.230    -.1757416    .7307405
          L3event |   .0297924   .2288013     0.13   0.896    -.4195312    .4791161
          L4event |   .9291061   .6067654     1.53   0.126    -.2624696    2.120682
          L5event |   2.317612   .8693411     2.67   0.008     .6103863    4.024839
          L6event |   1.112957   .6732268     1.65   0.099    -.2091365    2.435051
          L7event |   2.169864   .9985502     2.17   0.030     .2088948    4.130833
        F7event_n |   .5996441   .2954796     2.03   0.043     .0193764    1.179912
        F6event_n |   .2794943   .2580542     1.08   0.279    -.2272768    .7862654
        F5event_n |   .5317725   .2374788     2.24   0.025     .0654077    .9981372
        F4event_n |    .142436   .2113269     0.67   0.501    -.2725713    .5574432
        F3event_n |   .2149818    .168232     1.28   0.202     -.115395    .5453586
        F2event_n |   .3703578   .1185245     3.12   0.002     .1375975     .603118
        L0event_n |   .0985905   .1772282     0.56   0.578     -.249453    .4466341
        L1event_n |  -.0870787   .1967825    -0.44   0.658    -.4735233    .2993659
        L2event_n |  -.1369718   .2497894    -0.55   0.584    -.6275123    .3535686
        L3event_n |  -.4923998   .2096802    -2.35   0.019    -.9041732   -.0806265
        L4event_n |   .3662167   .4785345     0.77   0.444    -.5735373    1.305971
        L5event_n |   .6610827    .949421     0.70   0.487    -1.203406    2.525571
        L6event_n |   .5449906   .9323927     0.58   0.559    -1.286057    2.376038
        L7event_n |   2.894933   1.153866     2.51   0.012     .6289532    5.160914
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   1.775804   1.064223     1.67   0.096    -.3141349    3.865743
         ew_biodt |   .7656031   .0311875    24.55   0.000     .7043565    .8268497
        ew_dtmihi |   -.156699   .0527783    -2.97   0.003    -.2603458   -.0530521
         ew_ledig |   .4226294   .0691329     6.11   0.000     .2868651    .5583937
       ew_married |   .6387139   .0682493     9.36   0.000     .5046849    .7727429
        wb_anteil |  -.5356114   .0237656   -22.54   0.000    -.5822826   -.4889402
          wb_ausl |  -.0518028    .017875    -2.90   0.004    -.0869061   -.0166996
         wb_18t24 |  -.0442143   .0256521    -1.72   0.085    -.0945903    .0061617
         wb_25t34 |  -.0222497   .0167835    -1.33   0.185    -.0552095    .0107101
         wb_35t44 |   .0007721   .0208327     0.04   0.970    -.0401394    .0416836
         wb_45t59 |  -.0241078   .0194878    -1.24   0.217    -.0623783    .0141627
          avg_dur |   .0155349   .0219639     0.71   0.480    -.0275981    .0586679
          hh_kids |  -.1323264   .0365327    -3.62   0.000    -.2040699   -.0605829
mpreis_flats_rent |   .0126839    .023832     0.53   0.595    -.0341178    .0594855
            _cons |  -.8633665   9.914806    -0.09   0.931    -20.33422    18.60749
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      15.93
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9723
                                                  Adj R-squared   =     0.9660
                                                  Within R-sq.    =     0.1776
Number of clusters (sb_new)  =        618         Root MSE        =     1.6923

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.2691702    .364337    -0.74   0.460     -.984661    .4463207
          F6event |  -.0039809   .3237445    -0.01   0.990    -.6397556    .6317938
          F5event |   .2245628   .2537788     0.88   0.377    -.2738121    .7229378
          F4event |   .0053202   .1754027     0.03   0.976    -.3391386    .3497789
          F3event |  -.0576023   .1725061    -0.33   0.739    -.3963725    .2811679
          F2event |   .0114244   .1239433     0.09   0.927    -.2319775    .2548262
          L0event |  -1.041045   .2344832    -4.44   0.000    -1.501527    -.580563
          L1event |  -.9059063   .2339117    -3.87   0.000    -1.365266   -.4465466
          L2event |  -.7537849   .2566749    -2.94   0.003    -1.257847   -.2497226
          L3event |  -.2908028     .26673    -1.09   0.276    -.8146116     .233006
          L4event |   -.986577   .4410322    -2.24   0.026    -1.852683   -.1204707
          L5event |  -.9435364   .5883725    -1.60   0.109    -2.098992    .2119191
          L6event |   .5549024   .5664311     0.98   0.328    -.5574642    1.667269
          L7event |   .6607589   .7919001     0.83   0.404    -.8943873    2.215905
        F7event_n |  -.1021736   .2963253    -0.34   0.730     -.684102    .4797548
        F6event_n |   .1355813   .2734097     0.50   0.620    -.4013451    .6725078
        F5event_n |   .2735486   .2149315     1.27   0.204    -.1485373    .6956346
        F4event_n |   .2894879   .1608954     1.80   0.072     -.026481    .6054569
        F3event_n |   .2268897   .1610431     1.41   0.159    -.0893693    .5431488
        F2event_n |   .1533218    .122125     1.26   0.210    -.0865093    .3931528
        L0event_n |  -.2929834    .225444    -1.30   0.194    -.7357139    .1497472
        L1event_n |  -.5919104   .2012903    -2.94   0.003    -.9872075   -.1966133
        L2event_n |  -.4784198   .2337969    -2.05   0.041    -.9375539   -.0192857
        L3event_n |  -.4309985   .2693902    -1.60   0.110    -.9600314    .0980344
        L4event_n |  -.6891193   .3611911    -1.91   0.057    -1.398432    .0201935
        L5event_n |  -.9112677   .6660512    -1.37   0.172     -2.21927    .3967344
        L6event_n |   -1.80213   .7022406    -2.57   0.011    -3.181202   -.4230587
        L7event_n |  -1.139352    1.48071    -0.77   0.442    -4.047194     1.76849
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |  -1.073794   1.000265    -1.07   0.283    -3.038131    .8905429
         ew_biodt |   .3678513   .0281512    13.07   0.000     .3125675    .4231352
        ew_dtmihi |   .0723486   .0510235     1.42   0.157    -.0278521    .1725493
         ew_ledig |   .1913538   .0573419     3.34   0.001     .0787448    .3039629
       ew_married |   .4113809   .0594528     6.92   0.000     .2946266    .5281352
        wb_anteil |  -.2822272    .020504   -13.76   0.000    -.3224933   -.2419612
          wb_ausl |   .0139062   .0162716     0.85   0.393    -.0180482    .0458606
         wb_18t24 |   -.014877   .0288868    -0.52   0.607    -.0716054    .0418514
         wb_25t34 |    -.06485   .0189044    -3.43   0.001    -.1019747   -.0277252
         wb_35t44 |   .0110265   .0232828     0.47   0.636    -.0346968    .0567497
         wb_45t59 |   .0168463   .0213123     0.79   0.430    -.0250072    .0586997
          avg_dur |  -.0619425   .0210629    -2.94   0.003    -.1033062   -.0205789
          hh_kids |   -.047707   .0398515    -1.20   0.232     -.125968     .030554
mpreis_flats_rent |   .0262011   .0251057     1.04   0.297    -.0231018     .075504
            _cons |   15.54441   9.082688     1.71   0.088    -2.292326    33.38114
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      13.54
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9618
                                                  Adj R-squared   =     0.9532
                                                  Within R-sq.    =     0.2082
Number of clusters (sb_new)  =        618         Root MSE        =     1.6834

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .1183432   .3468867     0.34   0.733    -.5628784    .7995649
          F6event |   .2403991     .26449     0.91   0.364    -.2790107    .7598089
          F5event |  -.4488531    .263181    -1.71   0.089    -.9656922    .0679861
          F4event |  -.2279927   .1600822    -1.42   0.155    -.5423647    .0863792
          F3event |  -.0013602   .1536839    -0.01   0.993    -.3031671    .3004467
          F2event |  -.0277152   .1242943    -0.22   0.824    -.2718063     .216376
          L0event |   .5921367   .2179574     2.72   0.007     .1641085    1.020165
          L1event |   .8948499   .2293521     3.90   0.000     .4444445    1.345255
          L2event |   1.038832   .2625834     3.96   0.000     .5231659    1.554497
          L3event |   .4476467   .2612753     1.71   0.087      -.06545    .9607435
          L4event |   1.476819   .7863168     1.88   0.061    -.0673626    3.021001
          L5event |   1.433929   .5492797     2.61   0.009     .3552443    2.512613
          L6event |  -.5892294   1.107227    -0.53   0.595     -2.76362    1.585161
          L7event |  -.1583181   .7682498    -0.21   0.837     -1.66702    1.350383
        F7event_n |   .1890665   .3065522     0.62   0.538    -.4129457    .7910787
        F6event_n |   .1719418   .2050076     0.84   0.402    -.2306555     .574539
        F5event_n |   .2589307   .2420476     1.07   0.285    -.2164065    .7342678
        F4event_n |   .0826193   .1529189     0.54   0.589    -.2176854     .382924
        F3event_n |  -.0563749   .1478277    -0.38   0.703    -.3466814    .2339316
        F2event_n |   .3463948    .105151     3.29   0.001     .1398975    .5528921
        L0event_n |  -.1192724   .2027445    -0.59   0.557    -.5174253    .2788805
        L1event_n |   .1522694   .2175287     0.70   0.484     -.274917    .5794557
        L2event_n |  -.2485466     .23574    -1.05   0.292    -.7114967    .2144035
        L3event_n |   .2435621    .276495     0.88   0.379    -.2994232    .7865475
        L4event_n |  -.1898573   .6073989    -0.31   0.755    -1.382677    1.002962
        L5event_n |   -.933149   .3955941    -2.36   0.019    -1.710023   -.1562748
        L6event_n |  -.3040081   1.170238    -0.26   0.795     -2.60214    1.994124
        L7event_n |   1.130816   .7274285     1.55   0.121    -.2977202    2.559352
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   2.328426   1.335737     1.74   0.082    -.2947164    4.951569
         ew_biodt |   .3808138   .0297124    12.82   0.000     .3224642    .4391634
        ew_dtmihi |  -.2364124   .0599368    -3.94   0.000    -.3541173   -.1187075
         ew_ledig |   .2056025     .08018     2.56   0.011     .0481436    .3630613
       ew_married |   .2024837   .0800558     2.53   0.012     .0452688    .3596986
        wb_anteil |  -.2351342   .0225142   -10.44   0.000    -.2793479   -.1909205
          wb_ausl |   -.068942   .0147564    -4.67   0.000    -.0979208   -.0399632
         wb_18t24 |  -.0277972   .0275044    -1.01   0.313    -.0818107    .0262163
         wb_25t34 |   .0519659   .0195934     2.65   0.008      .013488    .0904438
         wb_35t44 |  -.0109049   .0252486    -0.43   0.666    -.0604885    .0386786
         wb_45t59 |  -.0388582   .0206112    -1.89   0.060    -.0793349    .0016184
          avg_dur |   .0378454   .0247137     1.53   0.126    -.0106878    .0863786
          hh_kids |   -.060092   .0416699    -1.44   0.150    -.1419241    .0217401
mpreis_flats_rent |  -.0184161   .0234685    -0.78   0.433     -.064504    .0276718
            _cons |  -9.928743   11.36253    -0.87   0.383    -32.24266    12.38518
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      35.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9905
                                                  Adj R-squared   =     0.9884
                                                  Within R-sq.    =     0.4510
Number of clusters (sb_new)  =        618         Root MSE        =     1.6108

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1508272   .3069482    -0.49   0.623     -.753617    .4519626
          F6event |   .2364175    .270673     0.87   0.383    -.2951346    .7679695
          F5event |  -.2242899   .2564043    -0.87   0.382    -.7278207     .279241
          F4event |  -.2226722   .1695107    -1.31   0.189    -.5555601    .1102157
          F3event |  -.0589627   .1588485    -0.37   0.711     -.370912    .2529865
          F2event |  -.0162904   .1350395    -0.12   0.904    -.2814831    .2489024
          L0event |  -.4489079   .1654045    -2.71   0.007    -.7737319   -.1240839
          L1event |  -.0110561   .2039206    -0.05   0.957    -.4115188    .3894066
          L2event |   .2850469   .2200576     1.30   0.196    -.1471059    .7171996
          L3event |   .1568443   .2379021     0.66   0.510    -.3103518    .6240403
          L4event |   .4902432   .7790715     0.63   0.529     -1.03971    2.020196
          L5event |    .490392   .6482042     0.76   0.450    -.7825618    1.763346
          L6event |  -.0343274   .8358739    -0.04   0.967     -1.67583    1.607175
          L7event |   .5024413   1.012556     0.50   0.620    -1.486032    2.490915
        F7event_n |   .0868927   .2577384     0.34   0.736    -.4192582    .5930435
        F6event_n |   .3075232   .2528151     1.22   0.224    -.1889593    .8040057
        F5event_n |   .5324793    .236465     2.25   0.025     .0681054    .9968532
        F4event_n |   .3721073   .1913228     1.94   0.052    -.0036155      .74783
        F3event_n |   .1705148   .1532662     1.11   0.266     -.130472    .4715015
        F2event_n |   .4997165   .1305131     3.83   0.000     .2434127    .7560202
        L0event_n |  -.4122557    .164684    -2.50   0.013     -.735665   -.0888465
        L1event_n |   -.439641   .1929517    -2.28   0.023    -.8185628   -.0607193
        L2event_n |  -.7269664   .2091294    -3.48   0.001    -1.137658   -.3162748
        L3event_n |  -.1874361   .2224364    -0.84   0.400    -.6242604    .2493882
        L4event_n |  -.8789753   .5631979    -1.56   0.119    -1.984993    .2270419
        L5event_n |  -1.844417    .531982    -3.47   0.001    -2.889132   -.7997017
        L6event_n |  -2.106135   .7548061    -2.79   0.005    -3.588435   -.6238343
        L7event_n |   -.008538   1.493375    -0.01   0.995    -2.941252    2.924176
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
        ln_ew_ges |   1.254633   .9704504     1.29   0.197    -.6511537    3.160419
         ew_biodt |   .7486652   .0310228    24.13   0.000     .6877422    .8095882
        ew_dtmihi |  -.1640637   .0524846    -3.13   0.002    -.2671339   -.0609935
         ew_ledig |   .3969565   .0681006     5.83   0.000     .2632194    .5306936
       ew_married |   .6138647   .0675257     9.09   0.000     .4812566    .7464728
        wb_anteil |  -.5173614   .0237858   -21.75   0.000    -.5640723   -.4706506
          wb_ausl |  -.0550358   .0175053    -3.14   0.002     -.089413   -.0206585
         wb_18t24 |  -.0426742   .0248812    -1.72   0.087    -.0915364     .006188
         wb_25t34 |  -.0128841    .016632    -0.77   0.439    -.0455462    .0197781
         wb_35t44 |   .0001216   .0207103     0.01   0.995    -.0405496    .0407927
         wb_45t59 |   -.022012   .0191735    -1.15   0.251    -.0596652    .0156413
          avg_dur |  -.0240971   .0231103    -1.04   0.297    -.0694816    .0212873
          hh_kids |  -.1077991   .0344671    -3.13   0.002    -.1754861   -.0401121
mpreis_flats_rent |    .007785   .0232761     0.33   0.738     -.037925    .0534949
            _cons |    5.61565   9.274615     0.61   0.545    -12.59799    23.82929
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         * PLOT: FIGURE 10. Effect Heterogeneity by Precinct Characteristics (Triple Difference E
> stimates)
.         grc1leg z_wb_60plus_urne_pos            z_wb_60plus_tot_req z_wb_60plus, leg(z_wb_60plus
> ) pos(12) col(2) imargins(small) xcommon ycommon name(gr_z_wb_60plus, replace) ///
>         title("{bf:Panel A.} Heterogeneity by % Electorate Aged 60+"    ,nobox span bexpand just
> ification(left) size(small))

.         gr_edit .plotregion1.graph3.draw_view.setstyle, style(no)               // erase extra p
> lot

.         grc1leg z_wb_18t24_urne_pos             z_wb_18t24_tot_req, row(1) imargins(small) xcomm
> on ycommon name(gr_z_wb_18t24, replace) ///
>         title("{bf:Panel B.} Heterogeneity by % Electorate Aged 18-24"  ,nobox span bexpand just
> ification(left) size(small))    

.         grc1leg z_hh_kids_urne_pos              z_hh_kids_tot_req, row(1)               imargins
> (small) xcommon ycommon name(gr_z_hh_kids, replace) ///
>         title("{bf:Panel C.} Heterogeneity by % Households with Children",nobox span bexpand jus
> tification(left) size(small))   

.         grc1leg z_mpreis_rent_urne_pos  z_mpreis_rent_tot_req, row(1)   imargins(small) xcommon 
> ycommon name(gr_z_mpreis_rent, replace) ///
>         title("{bf:Panel D.} Heterogeneity by Average Quoted Rent per sqm",nobox span bexpand ju
> stification(left) size(small)) 

.         grc1leg z_ew_dtmihi_urne_pos            z_ew_dtmihi_tot_req, row(1)     imargins(small) 
> xcommon ycommon name(gr_z_ew_dtmihi, replace) ///
>         title("{bf:Panel E.} Heterogeneity by % Germans with Migrant Background",nobox span bexp
> and justification(left) size(small)) 

.         
.         grc1leg gr_z_wb_60plus gr_z_wb_18t24 gr_z_hh_kids gr_z_mpreis_rent gr_z_ew_dtmihi , col(
> 1) pos(12) imargins(zero)

.         gr_edit .legend.Edit , style(rows(1)) style(cols(0)) keepstyles // legend

.         gr_edit .legend.title.DragBy -3.1 -12.6 

.         gr_edit .style.editstyle declared_ysize(8.4) editcopy                   // size 

.         graph export "$figures/Figure_10_ES_het_z_charac_top5.pdf", replace             
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_10_ES_
    > het_z_charac_top5.pdf saved as PDF format

.                         
.         
.                 // Test if overall effects are different from zero: % Electorate aged 60+
.                 estimates restore z_wb_60plus_tot_req
(results z_wb_60plus_tot_req are active now)

.                 lincom (L0event+L0event_n + L1event+L1event_n+L2event+L2event_n)

 ( 1)  L0event + L1event + L2event + L0event_n + L1event_n + L2event_n = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -2.368317   .7654236    -3.09   0.002    -3.871468   -.8651654
------------------------------------------------------------------------------

.                 test (L0event+L0event_n + L1event+L1event_n+L2event+L2event_n)==0

 ( 1)  L0event + L1event + L2event + L0event_n + L1event_n + L2event_n = 0

       F(  1,   617) =    9.57
            Prob > F =    0.0021

.                 test (L0event+L0event_n)=0

 ( 1)  L0event + L0event_n = 0

       F(  1,   617) =   20.54
            Prob > F =    0.0000

.                 test L1event+L1event_n=0

 ( 1)  L1event + L1event_n = 0

       F(  1,   617) =    6.76
            Prob > F =    0.0096

.                 test L2event+L2event_n=0

 ( 1)  L2event + L2event_n = 0

       F(  1,   617) =    1.78
            Prob > F =    0.1828

.                 test (L0event+L0event_n)=(L1event+L1event_n)=(L2event+L2event_n)=0 // jointly !=
> 0 in 0,1,2

 ( 1)  L0event - L1event + L0event_n - L1event_n = 0
 ( 2)  L0event - L2event + L0event_n - L2event_n = 0
 ( 3)  L0event + L0event_n = 0

       F(  3,   617) =    7.47
            Prob > F =    0.0001

.                 test (L1event+L1event_n)=(L2event+L2event_n)=0 // jointly !=0 in 1,2

 ( 1)  L1event - L2event + L1event_n - L2event_n = 0
 ( 2)  L1event + L1event_n = 0

       F(  2,   617) =    3.58
            Prob > F =    0.0284

. 
.         * TABLE E6. Heterogeneity by Precinct Characteristics–Triple Difference Estimates
.         // EXPORT Table (ONLY triple diff coefs)
.                 local n : word count $het

.                 foreach v in urne pos_req tot_req{
  2.                         qui outreg, replay(`v'_`:word 1 of $het') store(`v')
  3.                         forvalues j=2/`n' {
  4.                                 qui outreg, replay(`v') merge(`v'_`:word `j' of $het')
  5.                         }
  6.                 }

.                 qui outreg, replay(urne) title("&(1)&(2)& (3)&(4)& (5)& (6)"\ "\midrule" \ "\mul
> ticolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}" \"\midrule") ///
>                         addrow("\midrule" \ "\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turn
> out via Mail}" \ "\midrule") store(tab)

.                 qui outreg, replay(tab) append(pos_req) addrow("\midrule" \ "\multicolumn{3}{l}{
> \textbf{Panel C:} Effect on Total Turnout}" \ "\midrule") store(tab)

.                 outreg using "$tables/Table_E6_ES_het_z_charac", replay(tab) append(tot_req) fra
> gment tex replace
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_E6
> _ES_het_z_charac.tex not found)
                                   &(1)&(2)& (3)&(4)& (5)& (6)
                                             \midrule
           \multicolumn{3}{l}{\textbf{Panel A:} Effect on Turnout at the Polling Place}
                                             \midrule
                                           {hline 259}
                                                                   (a) % electorate aged 60+  (b) 
> % electorate aged 18-24  (c) % households with children  (d) Average quoted rent per sqm  (e) % 
> non-native German residents  (f) Average duration of residence 
                                           {hline 259}
\hspace{.7cm}Reassignment (#t-4#)                                            0.24                 
>       -0.31*                         -0.24                            0.05                      
>         -0.06                              0.29                
                                                                            (0.17)                
>       (0.13)                         (0.16)                          (0.14)                     
>        (0.18)                             (0.16)               
\hspace{.7cm}Reassignment (#t-3#)                                            0.22                 
>        -0.12                         -0.18                            0.06                      
>         0.09                               0.23                
                                                                            (0.17)                
>       (0.12)                         (0.18)                          (0.12)                     
>        (0.19)                             (0.16)               
\hspace{.7cm}Reassignment (#t-2#)                                            0.19                 
>        -0.21                         -0.10                            -0.02                     
>         0.00                               0.15                
                                                                            (0.12)                
>       (0.12)                         (0.15)                          (0.11)                     
>        (0.14)                             (0.12)               
\hspace{.7cm}Reassignment (#t+0#)                                            -0.43                
>        0.33                           0.38                            -0.17                     
>        0.74**                              -0.29               
                                                                            (0.23)                
>       (0.18)                         (0.26)                          (0.26)                     
>        (0.25)                             (0.23)               
\hspace{.7cm}Reassignment (#t+1#)                                           -0.49*                
>       0.59**                          0.26                            -0.02                     
>        0.55**                             -0.59**              
                                                                            (0.21)                
>       (0.19)                         (0.21)                          (0.26)                     
>        (0.20)                             (0.20)               
\hspace{.7cm}Reassignment (#t+2#)                                            -0.17                
>        0.45*                          0.17                            -0.05                     
>        0.69**                             -0.48*               
                                                                            (0.26)                
>       (0.22)                         (0.36)                          (0.24)                     
>        (0.25)                             (0.23)               
R2                                                                           0.97                 
>        0.97                           0.97                            0.97                      
>         0.97                               0.97                
N                                                                            4,666                
>        4,666                         4,666                            4,666                     
>         4,666                              4,666               
\midrule                                                                                          
>                                                                                                 
>                                                                
\multicolumn{3}{l}{\textbf{Panel B:} Effect on Turnout via Mail}                                  
>                                                                                                 
>                                                                
\midrule                                                                                          
>                                                                                                 
>                                                                
\hspace{.7cm}Reassignment (#t-4#)                                            -0.03                
>        -0.10                          0.16                            -0.10                     
>         0.20                               0.08                
                                                                            (0.16)                
>       (0.16)                         (0.18)                          (0.14)                     
>        (0.19)                             (0.15)               
\hspace{.7cm}Reassignment (#t-3#)                                            -0.21                
>        0.06                           0.23                            -0.09                     
>         0.12                               -0.06               
                                                                            (0.17)                
>       (0.12)                         (0.20)                          (0.12)                     
>        (0.19)                             (0.15)               
\hspace{.7cm}Reassignment (#t-2#)                                            0.08                 
>        0.10                           0.26                            -0.12                     
>        0.37**                             0.35**               
                                                                            (0.13)                
>       (0.14)                         (0.14)                          (0.09)                     
>        (0.13)                             (0.11)               
\hspace{.7cm}Reassignment (#t+0#)                                            -0.23                
>        -0.09                         -0.35                            0.29                      
>       -0.64***                             -0.12               
                                                                            (0.20)                
>       (0.17)                         (0.21)                          (0.21)                     
>        (0.19)                             (0.20)               
\hspace{.7cm}Reassignment (#t+1#)                                            -0.28                
>       -0.41*                         -0.11                            0.02                      
>        -0.63**                             0.15                
                                                                            (0.22)                
>       (0.18)                         (0.20)                          (0.21)                     
>        (0.21)                             (0.22)               
\hspace{.7cm}Reassignment (#t+2#)                                           -0.58*                
>        -0.09                         -0.11                            0.03                      
>       -0.83***                             -0.25               
                                                                            (0.23)                
>       (0.19)                         (0.28)                          (0.21)                     
>        (0.20)                             (0.24)               
R2                                                                           0.96                 
>        0.96                           0.96                            0.96                      
>         0.96                               0.96                
N                                                                            4,666                
>        4,666                         4,666                            4,666                     
>         4,666                              4,666               
\midrule                                                                                          
>                                                                                                 
>                                                                
\multicolumn{3}{l}{\textbf{Panel C:} Effect on Total Turnout}                                     
>                                                                                                 
>                                                                
\midrule                                                                                          
>                                                                                                 
>                                                                
\hspace{.7cm}Reassignment (#t-4#)                                            0.22                 
>       -0.41*                         -0.08                            -0.05                     
>         0.14                               0.37                
                                                                            (0.17)                
>       (0.20)                         (0.16)                          (0.15)                     
>        (0.21)                             (0.19)               
\hspace{.7cm}Reassignment (#t-3#)                                            0.01                 
>        -0.06                          0.05                            -0.02                     
>         0.21                               0.17                
                                                                            (0.17)                
>       (0.12)                         (0.15)                          (0.11)                     
>        (0.17)                             (0.15)               
\hspace{.7cm}Reassignment (#t-2#)                                            0.27*                
>        -0.11                          0.16                            -0.14                     
>        0.37**                             0.50***              
                                                                            (0.14)                
>       (0.13)                         (0.11)                          (0.10)                     
>        (0.12)                             (0.13)               
\hspace{.7cm}Reassignment (#t+0#)                                          -0.65***               
>        0.24                           0.03                            0.12                      
>         0.10                              -0.41*               
                                                                            (0.15)                
>       (0.12)                         (0.16)                          (0.15)                     
>        (0.18)                             (0.16)               
\hspace{.7cm}Reassignment (#t+1#)                                          -0.77***               
>        0.17                           0.15                            -0.00                     
>         -0.09                             -0.44*               
                                                                            (0.18)                
>       (0.14)                         (0.14)                          (0.17)                     
>        (0.20)                             (0.19)               
\hspace{.7cm}Reassignment (#t+2#)                                          -0.75***               
>        0.35*                          0.05                            -0.02                     
>         -0.14                            -0.73***              
                                                                            (0.19)                
>       (0.16)                         (0.24)                          (0.16)                     
>        (0.25)                             (0.21)               
R2                                                                           0.99                 
>        0.99                           0.99                            0.99                      
>         0.99                               0.99                
N                                                                            4,666                
>        4,666                         4,666                            4,666                     
>         4,666                              4,666               
                                           {hline 259}


.                 cleantex "$tables/Table_E6_ES_het_z_charac.tex" , nodisplay     replace

.                 
.         
.                 
.                 
. 
. ********************************************************************************
.  // Robustness: Reestimate DDD, controlling  for log distance (Figure D13) //           
. ********************************************************************************
.         // labels for figure
.         local j=1

.         foreach v of varlist $het {
  2.                 local lb: var lab `v'
  3.                 local x = "`: word `j' of `c(alpha)''"
  4.                 lab var `v' "(`x') `lb'"
  5.                 local ++j
  6.         }

.         
. estimates clear

. outreg, clear

. foreach h of varlist $het  {
  2.         
.         // gen leads and lags
.         cap drop L* F*
  3.         forvalues l = 7(-1)1 {
  4.                 gen F`l'event = K==-`l'
  5.                 gen F`l'event_n = F`l'event * `h'
  6.                 lab var F`l'event_n "\hspace{.7cm}Reassignment (#t-`l'#)"               
  7.         }       
  8.         forvalues l = 0/7 {
  9.                 gen L`l'event = K==`l'
 10.                 gen L`l'event_n = L`l'event * `h'
 11.                 lab var L`l'event_n "\hspace{.7cm}Reassignment (#t+`l'#)"
 12.         }
 13.         order *event_n, last
 14.         order F1event*, last
 15.         
. 
.         // Estimate ES: base levels + interactions + CONTROL for log sreet dist
.         foreach v in urne pos_req tot_req{
 16.         
.                  reghdfe turnout_`v' F7event-L7event F7event_n-L7event_n F1event F1event_n ln_st
> reet_dist $ctr $wgt if smpl_trim ==1, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
 17.                 estimates store `h'_`v'
 18.                 qui outreg,  $opt  keep(F4event_n-L2event_n) store(`v'_`h') ctitle("","`:var 
> lab `h''")         
 19.         }
 20.         
.                 // PLOT: All 3 outcomes in one plot
.                 event_plot  `h'_urne `h'_pos_req `h'_tot_req , ///
>                 stub_lag(L#event_n ) stub_lead(F#event_n ) plottype(scatter) ciplottype(rcap) //
> /
>                 together perturb(-0.23(0.23)0.23) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(small)) xlab
> el(-4(1)2) xtitle("Election since reassignment", size(small)) ///
>                         legend(pos(12) order(1 "Polling place turnout" 3 "Mail-in turnout" 5 "To
> tal turnout" ) size(vsmall) col(1) region(style(none)) title("{bf:Outcomes:}", pos(11) just(left
> ) span bexpand size(vsmall))) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         title("`:var lab `h''", size(small)) ///
>                         name(`h', replace) ) ///
>                 lag_opt1(msymbol(S) msize(2.5pt) color(navy))           lag_ci_opt1(color(navy))
>  ///
>                 lag_opt2(msymbol(O) msize(2.5pt) color(maroon))         lag_ci_opt2(color(maroon
> )) ///  
>                 lag_opt3(msymbol(Oh) msize(2.5pt) color(black))         lag_ci_opt3(color(black)
> )               
 21.                 
.                 // PLOT: 2 outcomes (mail, pp)
.                 event_plot  `h'_urne `h'_pos_req /*`h'_tot_req*/ , ///
>                 stub_lag(L#event_n ) stub_lead(F#event_n ) plottype(connect) ciplottype(rcap) //
> /
>                 together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(ytitle("Voter turnout in %""(estimates)", size(small)) xlabel(-4(1)2) 
> xtitle("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) order(1 "Polling place turnout" 3 "Mail-in turnout") row(1) regio
> n(style(none))) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         name(`h'_urne_pos, replace) ) ///
>                 lag_opt1(msymbol(S) msize(2.5pt) color(navy))   lag_ci_opt1(color(navy)) ///
>                 lag_opt2(msymbol(O) msize(2.5pt) color(maroon)) lag_ci_opt2(color(maroon)) 
 22. 
.                 // PLOT: 1 outcome (overall)
.                 event_plot  `h'_tot_req, ///
>                 stub_lag(L#event_n ) stub_lead(F#event_n ) plottype(connect) ciplottype(rcap) //
> /
>                 together trimlead(4) trimlag(2)  noautolegend ///
>                 graph_opt(ytitle("Voter turnout in %""(estimates)", size(small)) xlabel(-4(1)2) 
> xtitle("Election since reassignment", size(medsmall)) ///
>                         legend(pos(11) order(1 "Total turnout") row(1) region(style(none))) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         name(`h'_tot_req,replace) ) ///
>                 lag_opt1(msymbol(Oh) msize(2.5pt) color(black))         lag_ci_opt1(color(black)
> ) 
 23.         
.         
. }
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      23.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9756
                                                  Adj R-squared   =     0.9701
                                                  Within R-sq.    =     0.2756
Number of clusters (sb_new)  =        618         Root MSE        =     1.5884

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1003785   .3333936    -0.30   0.763    -.7551022    .5543452
          F6event |   .0184463   .2994773     0.06   0.951     -.569672    .6065646
          F5event |   .2108847    .241411     0.87   0.383    -.2632021    .6849716
          F4event |   .0067546   .1687836     0.04   0.968    -.3247055    .3382146
          F3event |  -.0949464   .1680952    -0.56   0.572    -.4250544    .2351617
          F2event |   .0340539   .1193537     0.29   0.775    -.2003349    .2684427
          L0event |  -.6304664   .2108973    -2.99   0.003     -1.04463   -.2163029
          L1event |  -.6528073   .2034601    -3.21   0.001    -1.052365   -.2532491
          L2event |  -.4078079   .2301069    -1.77   0.077    -.8596956    .0440797
          L3event |   -.310882   .2453903    -1.27   0.206    -.7927835    .1710194
          L4event |  -.7411763   .4846802    -1.53   0.127    -1.692999    .2106466
          L5event |  -.8784382   .5134689    -1.71   0.088    -1.886797    .1299204
          L6event |   1.222144   .7140452     1.71   0.087    -.1801095    2.624398
          L7event |   .5458405   .7221151     0.76   0.450    -.8722609    1.963942
        F7event_n |   .1126258   .3264999     0.34   0.730      -.52856    .7538116
        F6event_n |   .2018504   .2882552     0.70   0.484    -.3642297    .7679306
        F5event_n |   .2454345   .2017243     1.22   0.224     -.150715     .641584
        F4event_n |    .256717   .1636946     1.57   0.117    -.0647492    .5781832
        F3event_n |   .2439887   .1740531     1.40   0.161    -.0978195    .5857969
        F2event_n |   .1998342   .1163628     1.72   0.086    -.0286809    .4283492
        L0event_n |    -.29107   .1907299    -1.53   0.128    -.6656285    .0834885
        L1event_n |  -.3287822    .186205    -1.77   0.078    -.6944546    .0368902
        L2event_n |  -.0245701   .2351854    -0.10   0.917     -.486431    .4372908
        L3event_n |   .0143929   .2640104     0.05   0.957     -.504075    .5328608
        L4event_n |    -.42159   .4540359    -0.93   0.353    -1.313233    .4700531
        L5event_n |  -.7238744   .6177804    -1.17   0.242    -1.937082    .4893327
        L6event_n |  -.2952085   .5713307    -0.52   0.606    -1.417197    .8267799
        L7event_n |  -4.153861   1.296928    -3.20   0.001     -6.70079   -1.606932
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -3.356121   .2474979   -13.56   0.000    -3.842161   -2.870081
        ln_ew_ges |  -1.683039    .855005    -1.97   0.049    -3.362112   -.0039664
         ew_biodt |   .3678037   .0261255    14.08   0.000     .3164981    .4191094
        ew_dtmihi |   .0317882   .0481334     0.66   0.509    -.0627371    .1263134
         ew_ledig |   .2273045   .0495404     4.59   0.000     .1300162    .3245927
       ew_married |   .3997331   .0512696     7.80   0.000      .299049    .5004171
        wb_anteil |  -.2879088   .0190209   -15.14   0.000    -.3252623   -.2505554
          wb_ausl |    .016628   .0150659     1.10   0.270    -.0129587    .0462146
         wb_18t24 |  -.0069777   .0277512    -0.25   0.802    -.0614759    .0475205
         wb_25t34 |  -.0395919   .0176559    -2.24   0.025    -.0742648    -.004919
         wb_35t44 |   .0089811   .0211768     0.42   0.672    -.0326063    .0505684
         wb_45t59 |   .0234492   .0204605     1.15   0.252    -.0167315    .0636299
          avg_dur |  -.0237592   .0197395    -1.20   0.229    -.0625239    .0150055
          hh_kids |    -.01944    .036659    -0.53   0.596    -.0914316    .0525516
mpreis_flats_rent |   .0333328   .0239465     1.39   0.164    -.0136937    .0803592
            _cons |   16.16595   7.831844     2.06   0.039      .785644    31.54625
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      69.08
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9647
                                                  Adj R-squared   =     0.9567
                                                  Within R-sq.    =     0.2684
Number of clusters (sb_new)  =        618         Root MSE        =     1.6183

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0021743   .3252839     0.01   0.995    -.6366236    .6409722
          F6event |   .2201388    .250014     0.88   0.379    -.2708427    .7111204
          F5event |  -.4834124   .2584827    -1.87   0.062     -.991025    .0242002
          F4event |  -.2630387   .1583229    -1.66   0.097    -.5739557    .0478783
          F3event |  -.0037241   .1516284    -0.02   0.980    -.3014945    .2940462
          F2event |  -.0686971   .1238174    -0.55   0.579    -.3118517    .1744575
          L0event |   .2440057   .2049733     1.19   0.234    -.1585241    .6465355
          L1event |   .6657797   .2042813     3.26   0.001     .2646088    1.066951
          L2event |   .8006624   .2289631     3.50   0.001      .351021    1.250304
          L3event |   .4506856   .2375538     1.90   0.058    -.0158265    .9171976
          L4event |    .565294   .8420675     0.67   0.502    -1.088372     2.21896
          L5event |   1.428809   .4864292     2.94   0.003     .4735519    2.384067
          L6event |  -.8888554   .6867928    -1.29   0.196     -2.23759    .4598794
          L7event |  -.5815095   .6429095    -0.90   0.366    -1.844066    .6810466
        F7event_n |   .2179693   .2557121     0.85   0.394    -.2842022    .7201408
        F6event_n |    .173135   .2105017     0.82   0.411    -.2402517    .5865216
        F5event_n |   .0733124   .2239254     0.33   0.743     -.366436    .5130608
        F4event_n |  -.0362307   .1602563    -0.23   0.821    -.3509446    .2784832
        F3event_n |  -.2249754   .1728364    -1.30   0.194    -.5643943    .1144436
        F2event_n |   .0757495   .1282793     0.59   0.555    -.1761675    .3276666
        L0event_n |  -.3335036    .177292    -1.88   0.060    -.6816725    .0146653
        L1event_n |  -.4069812   .1918039    -2.12   0.034    -.7836488   -.0303135
        L2event_n |  -.6958641   .2128826    -3.27   0.001    -1.113926   -.2778017
        L3event_n |  -.4022809   .2547149    -1.58   0.115    -.9024942    .0979324
        L4event_n |  -1.697875   .6905191    -2.46   0.014    -3.053927   -.3418221
        L5event_n |  -1.442488   .4789447    -3.01   0.003    -2.383048   -.5019286
        L6event_n |  -2.563241   .6714177    -3.82   0.000    -3.881782     -1.2447
        L7event_n |   .5706702   .6158385     0.93   0.354    -.6387235    1.780064
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |   2.643356   .2468967    10.71   0.000     2.158496    3.128215
        ln_ew_ges |   2.737394   1.256426     2.18   0.030     .2700039    5.204785
         ew_biodt |    .386062    .028086    13.75   0.000     .3309064    .4412177
        ew_dtmihi |  -.2056827   .0588295    -3.50   0.001     -.321213   -.0901524
         ew_ledig |   .1760051   .0707232     2.49   0.013     .0371176    .3148926
       ew_married |    .198827   .0727439     2.73   0.006     .0559713    .3416827
        wb_anteil |  -.2414326   .0212396   -11.37   0.000    -.2831432    -.199722
          wb_ausl |  -.0727855   .0143365    -5.08   0.000    -.1009397   -.0446313
         wb_18t24 |  -.0290362   .0272089    -1.07   0.286    -.0824694    .0243971
         wb_25t34 |   .0449903   .0184734     2.44   0.015      .008712    .0812686
         wb_35t44 |  -.0020871   .0231592    -0.09   0.928    -.0475676    .0433933
         wb_45t59 |  -.0352042   .0197814    -1.78   0.076    -.0740513    .0036429
          avg_dur |   .0397666   .0224998     1.77   0.078    -.0044188     .083952
          hh_kids |    -.07477   .0412329    -1.81   0.070    -.1557438    .0062038
mpreis_flats_rent |  -.0298825   .0227311    -1.31   0.189    -.0745221    .0147572
            _cons |  -10.28924   10.55734    -0.97   0.330    -31.02191    10.44344
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      51.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9906
                                                  Adj R-squared   =     0.9885
                                                  Within R-sq.    =     0.4567
Number of clusters (sb_new)  =        618         Root MSE        =     1.6025

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0982044    .308142    -0.32   0.750    -.7033387    .5069298
          F6event |   .2385844   .2611005     0.91   0.361    -.2741689    .7513377
          F5event |  -.2725272   .2563758    -1.06   0.288    -.7760022    .2309477
          F4event |  -.2562838    .169663    -1.51   0.131    -.5894707    .0769031
          F3event |  -.0986708   .1611725    -0.61   0.541    -.4151839    .2178424
          F2event |  -.0346428   .1354502    -0.26   0.798    -.3006421    .2313565
          L0event |  -.3864604   .1622108    -2.38   0.017    -.7050125   -.0679082
          L1event |   .0129727   .2045948     0.06   0.949    -.3888139    .4147593
          L2event |   .3928547   .2206264     1.78   0.075    -.0404149    .8261244
          L3event |    .139804    .232672     0.60   0.548    -.3171211     .596729
          L4event |  -.1758808   .8311726    -0.21   0.832    -1.808151     1.45639
          L5event |   .5503712   .6307523     0.87   0.383    -.6883105    1.789053
          L6event |   .3332875   .6917331     0.48   0.630    -1.025149    1.691724
          L7event |  -.0356676   .8478536    -0.04   0.966    -1.700696    1.629361
        F7event_n |   .3305946   .3044559     1.09   0.278    -.2673009    .9284901
        F6event_n |   .3749857   .2359476     1.59   0.113     -.088372    .8383434
        F5event_n |   .3187475    .209698     1.52   0.129    -.0930608    .7305557
        F4event_n |   .2204866   .1656291     1.33   0.184    -.1047786    .5457518
        F3event_n |   .0190133   .1724343     0.11   0.912    -.3196159    .3576426
        F2event_n |   .2755838   .1374131     2.01   0.045     .0057298    .5454378
        L0event_n |  -.6245738   .1470704    -4.25   0.000     -.913393   -.3357546
        L1event_n |  -.7357633   .1858563    -3.96   0.000    -1.100751   -.3707757
        L2event_n |  -.7204343   .1891309    -3.81   0.000    -1.091853   -.3490159
        L3event_n |  -.3878877   .2251034    -1.72   0.085    -.8299494     .054174
        L4event_n |  -2.119463   .6385455    -3.32   0.001    -3.373449   -.8654772
        L5event_n |  -2.166362   .6136214    -3.53   0.000    -3.371402   -.9613225
        L6event_n |  -2.858445     1.0368    -2.76   0.006     -4.89453   -.8223597
        L7event_n |  -3.583191   .8365557    -4.28   0.000    -5.226033    -1.94035
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -.7127651   .2058897    -3.46   0.001    -1.117095   -.3084357
        ln_ew_ges |   1.054355   1.033244     1.02   0.308    -.9747454    3.083456
         ew_biodt |   .7538658   .0309935    24.32   0.000     .6930002    .8147314
        ew_dtmihi |  -.1738944   .0523138    -3.32   0.001    -.2766291   -.0711597
         ew_ledig |   .4033097   .0704324     5.73   0.000     .2649935    .5416259
       ew_married |   .5985601   .0688227     8.70   0.000     .4634051    .7337152
        wb_anteil |  -.5293414   .0237938   -22.25   0.000     -.576068   -.4826148
          wb_ausl |  -.0561575   .0176272    -3.19   0.002    -.0907742   -.0215408
         wb_18t24 |  -.0360139   .0246432    -1.46   0.144    -.0844087    .0123809
         wb_25t34 |   .0053984   .0171766     0.31   0.753    -.0283333      .03913
         wb_35t44 |    .006894    .020855     0.33   0.741    -.0340615    .0478494
         wb_45t59 |   -.011755   .0198731    -0.59   0.554    -.0507822    .0272721
          avg_dur |   .0160074   .0214767     0.75   0.456    -.0261688    .0581836
          hh_kids |  -.0942101   .0345207    -2.73   0.007    -.1620025   -.0264177
mpreis_flats_rent |   .0034503   .0235376     0.15   0.884    -.0427733    .0496739
            _cons |   5.876699    9.70914     0.61   0.545    -13.19027    24.94366
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      19.07
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9756
                                                  Adj R-squared   =     0.9701
                                                  Within R-sq.    =     0.2765
Number of clusters (sb_new)  =        618         Root MSE        =     1.5875

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0932498   .3259893    -0.29   0.775    -.7334329    .5469333
          F6event |   .0636749   .2923856     0.22   0.828    -.5105168    .6378666
          F5event |   .2157494   .2383282     0.91   0.366    -.2522834    .6837823
          F4event |   .0047334   .1673241     0.03   0.977    -.3238604    .3333272
          F3event |  -.1075769   .1673774    -0.64   0.521    -.4362754    .2211216
          F2event |   .0257923   .1194528     0.22   0.829    -.2087911    .2603757
          L0event |  -.6230514   .2102817    -2.96   0.003    -1.036006   -.2100969
          L1event |  -.6672649   .2004414    -3.33   0.001    -1.060895   -.2736349
          L2event |  -.4410914    .227829    -1.94   0.053    -.8885057    .0063229
          L3event |  -.3759775   .2469441    -1.52   0.128    -.8609303    .1089753
          L4event |  -.7030689   .4321557    -1.63   0.104    -1.551743    .1456055
          L5event |  -.5732426   .6049209    -0.95   0.344    -1.761196    .6147108
          L6event |   1.114643   .6492935     1.72   0.087      -.16045    2.389736
          L7event |   .7395897   1.213392     0.61   0.542     -1.64329    3.122469
        F7event_n |  -.4698541   .3417216    -1.37   0.170    -1.140933    .2012243
        F6event_n |  -.3543943   .1441853    -2.46   0.014    -.6375477    -.071241
        F5event_n |  -.3324224   .1557506    -2.13   0.033     -.638288   -.0265568
        F4event_n |  -.3483401   .1247587    -2.79   0.005    -.5933432    -.103337
        F3event_n |  -.1448439   .1242152    -1.17   0.244    -.3887797    .0990918
        F2event_n |  -.2028571   .1171561    -1.73   0.084    -.4329301    .0272159
        L0event_n |   .2203952   .1623894     1.36   0.175    -.0985077    .5392981
        L1event_n |   .4174532   .1544854     2.70   0.007     .1140722    .7208342
        L2event_n |   .1969581   .1695775     1.16   0.246    -.1360609    .5299771
        L3event_n |   .2013501   .1703647     1.18   0.238     -.133215    .5359151
        L4event_n |   .2924095    .230281     1.27   0.205      -.15982    .7446391
        L5event_n |   .4943737   .2823792     1.75   0.080    -.0601672    1.048915
        L6event_n |   .0847422   1.232948     0.07   0.945    -2.336541    2.506025
        L7event_n |   1.222807   1.302804     0.94   0.348    -1.335661    3.781275
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -3.348546    .254523   -13.16   0.000    -3.848382   -2.848709
        ln_ew_ges |  -1.591433   .8593321    -1.85   0.065    -3.279003    .0961373
         ew_biodt |   .3684699   .0258985    14.23   0.000     .3176101    .4193297
        ew_dtmihi |   .0272341   .0489202     0.56   0.578    -.0688362    .1233044
         ew_ledig |   .2447663   .0498861     4.91   0.000     .1467992    .3427335
       ew_married |   .4197337    .052394     8.01   0.000     .3168415    .5226259
        wb_anteil |  -.2876306   .0188776   -15.24   0.000    -.3247028   -.2505584
          wb_ausl |   .0148144    .014867     1.00   0.319    -.0143816    .0440105
         wb_18t24 |   -.011468   .0270774    -0.42   0.672    -.0646431    .0417071
         wb_25t34 |  -.0456488   .0175803    -2.60   0.010    -.0801733   -.0111244
         wb_35t44 |   .0017478    .021322     0.08   0.935    -.0401247    .0436203
         wb_45t59 |   .0223823   .0206815     1.08   0.280    -.0182323     .062997
          avg_dur |   -.019125   .0205022    -0.93   0.351    -.0593876    .0211375
          hh_kids |  -.0182622   .0370034    -0.49   0.622      -.09093    .0544056
mpreis_flats_rent |   .0375338   .0234993     1.60   0.111    -.0086146    .0836822
            _cons |   13.99582    7.87588     1.78   0.076    -1.470957    29.46261
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      18.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9645
                                                  Adj R-squared   =     0.9565
                                                  Within R-sq.    =     0.2649
Number of clusters (sb_new)  =        618         Root MSE        =     1.6222

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0089176   .3290529     0.03   0.978    -.6372817    .6551169
          F6event |   .2384244   .2515332     0.95   0.344    -.2555406    .7323893
          F5event |  -.4741477   .2526975    -1.88   0.061    -.9703991    .0221037
          F4event |   -.221499   .1587879    -1.39   0.164    -.5333293    .0903313
          F3event |   .0451572   .1504786     0.30   0.764    -.2503551    .3406695
          F2event |  -.0775382   .1214734    -0.64   0.524    -.3160897    .1610133
          L0event |   .2956239    .207209     1.43   0.154    -.1112964    .7025443
          L1event |   .7252072   .2039245     3.56   0.000     .3247369    1.125677
          L2event |   .7949202   .2374883     3.35   0.001     .3285369    1.261304
          L3event |   .4519268   .2438309     1.85   0.064    -.0269123     .930766
          L4event |   .8833515   .6864914     1.29   0.199    -.4647915    2.231494
          L5event |   2.260567   .5764467     3.92   0.000     1.128531    3.392602
          L6event |  -.3773065   .5472705    -0.69   0.491    -1.452045    .6974321
          L7event |  -.4207886   .6815473    -0.62   0.537    -1.759222    .9176452
        F7event_n |  -.0688794   .3250623    -0.21   0.832     -.707242    .5694832
        F6event_n |   .0277744   .1408132     0.20   0.844    -.2487569    .3043058
        F5event_n |   .3324382   .1655327     2.01   0.045     .0073625    .6575139
        F4event_n |  -.0664579   .1722584    -0.39   0.700    -.4047418    .2718259
        F3event_n |   .0826652   .1202589     0.69   0.492    -.1535013    .3188316
        F2event_n |   .0925768   .1365382     0.68   0.498     -.175559    .3607127
        L0event_n |   -.009049   .1458082    -0.06   0.951    -.2953895    .2772916
        L1event_n |  -.2818113   .1601961    -1.76   0.079     -.596407    .0327845
        L2event_n |   .0998934   .1710669     0.58   0.559    -.2360505    .4358373
        L3event_n |  -.0107777   .1633154    -0.07   0.947    -.3314991    .3099437
        L4event_n |   .5523637   .3773639     1.46   0.144    -.1887097    1.293437
        L5event_n |  -.1210141   .3835654    -0.32   0.752    -.8742662    .6322379
        L6event_n |   3.573192   .6859761     5.21   0.000     2.226061    4.920323
        L7event_n |   1.109396   1.788421     0.62   0.535    -2.402733    4.621526
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |   2.600149    .251461    10.34   0.000     2.106326    3.093972
        ln_ew_ges |   3.012612   1.295735     2.33   0.020     .4680269    5.557197
         ew_biodt |   .3910261   .0282592    13.84   0.000     .3355303    .4465219
        ew_dtmihi |  -.2058756   .0587288    -3.51   0.000    -.3212081   -.0905431
         ew_ledig |   .1870812   .0720428     2.60   0.010     .0456023    .3285601
       ew_married |   .2135907   .0736981     2.90   0.004     .0688612    .3583202
        wb_anteil |  -.2427196   .0216363   -11.22   0.000    -.2852093     -.20023
          wb_ausl |  -.0711043   .0138904    -5.12   0.000    -.0983825   -.0438261
         wb_18t24 |  -.0286096   .0272555    -1.05   0.294    -.0821344    .0249153
         wb_25t34 |    .034722   .0178966     1.94   0.053    -.0004236    .0698677
         wb_35t44 |  -.0073965   .0230968    -0.32   0.749    -.0527544    .0379614
         wb_45t59 |  -.0427651   .0195663    -2.19   0.029    -.0811898   -.0043404
          avg_dur |   .0408619   .0223395     1.83   0.068    -.0030086    .0847325
          hh_kids |  -.0825139   .0408781    -2.02   0.044     -.162791   -.0022367
mpreis_flats_rent |  -.0207672   .0226476    -0.92   0.360    -.0652429    .0237085
            _cons |  -13.32911    10.7395    -1.24   0.215    -34.41952    7.761303
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      32.67
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9905
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4487
Number of clusters (sb_new)  =        618         Root MSE        =     1.6144

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0843323   .3184428    -0.26   0.791    -.7096954    .5410309
          F6event |   .3020986   .2695366     1.12   0.263    -.2272218    .8314189
          F5event |  -.2583979   .2597387    -0.99   0.320    -.7684771    .2516812
          F4event |  -.2167653   .1664538    -1.30   0.193      -.54365    .1101195
          F3event |    -.06242    .159587    -0.39   0.696    -.3758196    .2509796
          F2event |  -.0517455    .133131    -0.39   0.698    -.3131903    .2096993
          L0event |  -.3274272   .1607071    -2.04   0.042    -.6430264    -.011828
          L1event |   .0579425   .2038109     0.28   0.776    -.3423047    .4581898
          L2event |   .3538291    .225309     1.57   0.117    -.0886365    .7962946
          L3event |   .0759497   .2334888     0.33   0.745    -.3825794    .5344789
          L4event |   .1802831    .729038     0.25   0.805    -1.251413     1.61198
          L5event |   1.687324   .8103728     2.08   0.038     .0959011    3.278748
          L6event |   .7373348    .552977     1.33   0.183    -.3486105     1.82328
          L7event |   .3188023   1.088447     0.29   0.770    -1.818707    2.456312
        F7event_n |  -.5387323   .3350533    -1.61   0.108    -1.196715    .1192509
        F6event_n |  -.3266204    .131572    -2.48   0.013    -.5850037    -.068237
        F5event_n |   .0000155   .1470994     0.00   1.000    -.2888606    .2888916
        F4event_n |  -.4147983   .1926121    -2.15   0.032     -.793053   -.0365435
        F3event_n |  -.0621788    .118988    -0.52   0.601    -.2958494    .1714918
        F2event_n |  -.1102802   .1257719    -0.88   0.381    -.3572732    .1367128
        L0event_n |   .2113462   .1273695     1.66   0.098    -.0387841    .4614764
        L1event_n |   .1356422   .1370971     0.99   0.323    -.1335913    .4048756
        L2event_n |   .2968514   .1480871     2.00   0.045     .0060355    .5876673
        L3event_n |   .1905722   .1751338     1.09   0.277    -.1533585    .5345029
        L4event_n |   .8447731   .3236536     2.61   0.009     .2091769    1.480369
        L5event_n |   .3733589     .31789     1.17   0.241    -.2509186    .9976364
        L6event_n |    3.65793   1.070505     3.42   0.001     1.555655    5.760205
        L7event_n |   2.332202   1.113275     2.09   0.037     .1459343     4.51847
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -.7483968   .2203682    -3.40   0.001    -1.181159   -.3156341
        ln_ew_ges |   1.421179   1.060526     1.34   0.181    -.6614999    3.503858
         ew_biodt |   .7594961   .0312286    24.32   0.000     .6981688    .8208233
        ew_dtmihi |  -.1786414   .0522481    -3.42   0.001    -.2812471   -.0760356
         ew_ledig |   .4318476   .0706124     6.12   0.000      .293178    .5705173
       ew_married |   .6333245   .0687583     9.21   0.000     .4982959    .7683531
        wb_anteil |  -.5303502   .0239053   -22.19   0.000    -.5772957   -.4834046
          wb_ausl |  -.0562899   .0178612    -3.15   0.002    -.0913661   -.0212137
         wb_18t24 |  -.0400776    .024614    -1.63   0.104    -.0884149    .0082598
         wb_25t34 |  -.0109268   .0166667    -0.66   0.512    -.0436571    .0218036
         wb_35t44 |  -.0056487   .0210946    -0.27   0.789    -.0470745    .0357771
         wb_45t59 |  -.0203828   .0198745    -1.03   0.305    -.0594126     .018647
          avg_dur |   .0217369   .0223364     0.97   0.331    -.0221277    .0656016
          hh_kids |  -.1007762    .034325    -2.94   0.003    -.1681842   -.0333682
mpreis_flats_rent |   .0167666   .0232297     0.72   0.471    -.0288524    .0623855
            _cons |   .6667046   9.936457     0.07   0.947    -18.84667    20.18008
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      19.54
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9755
                                                  Adj R-squared   =     0.9700
                                                  Within R-sq.    =     0.2743
Number of clusters (sb_new)  =        618         Root MSE        =     1.5899

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0872666   .3138162    -0.28   0.781    -.7035438    .5290107
          F6event |    .028501   .2900894     0.10   0.922    -.5411812    .5981832
          F5event |   .2253287   .2431239     0.93   0.354     -.252122    .7027794
          F4event |   .0001603   .1679584     0.00   0.999    -.3296792    .3299998
          F3event |  -.0987355   .1674905    -0.59   0.556     -.427656     .230185
          F2event |   .0237862   .1217487     0.20   0.845    -.2153059    .2628784
          L0event |  -.5667399   .2100712    -2.70   0.007    -.9792811   -.1541988
          L1event |   -.613695   .2016748    -3.04   0.002    -1.009747   -.2176426
          L2event |  -.4496738   .2311356    -1.95   0.052    -.9035817    .0042341
          L3event |  -.4270258   .2528084    -1.69   0.092    -.9234949    .0694434
          L4event |   -.473981    .427783    -1.11   0.268    -1.314068    .3661062
          L5event |   .1348186   .5476352     0.25   0.806    -.9406363    1.210274
          L6event |   .5240959    .502761     1.04   0.298    -.4632343    1.511426
          L7event |    .969287   .8168009     1.19   0.236    -.6347599    2.573334
        F7event_n |   .5807689   .2999941     1.94   0.053    -.0083645    1.169902
        F6event_n |  -.0239181   .2969555    -0.08   0.936    -.6070843     .559248
        F5event_n |  -.0129856    .245235    -0.05   0.958    -.4945821     .468611
        F4event_n |  -.2777577   .1695647    -1.64   0.102    -.6107517    .0552362
        F3event_n |  -.1977769   .1834281    -1.08   0.281     -.557996    .1624421
        F2event_n |  -.0847349   .1464067    -0.58   0.563    -.3722507    .2027809
        L0event_n |   .2634635   .2129738     1.24   0.217     -.154778     .681705
        L1event_n |   .0804867   .1462585     0.55   0.582    -.2067382    .3677116
        L2event_n |   -.032667   .2271768    -0.14   0.886    -.4788004    .4134665
        L3event_n |  -.2684519   .3073314    -0.87   0.383    -.8719944    .3350905
        L4event_n |   .2812633    .453326     0.62   0.535    -.6089857    1.171512
        L5event_n |   .6576578   .5510599     1.19   0.233    -.4245227    1.739838
        L6event_n |  -1.088291    .575348    -1.89   0.059    -2.218169    .0415866
        L7event_n |   .2588884   1.265059     0.20   0.838    -2.225456    2.743233
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -3.365795   .2438238   -13.80   0.000     -3.84462    -2.88697
        ln_ew_ges |  -1.802904   .8680336    -2.08   0.038    -3.507563   -.0982458
         ew_biodt |    .371688   .0262588    14.15   0.000     .3201205    .4232554
        ew_dtmihi |   .0251208   .0495361     0.51   0.612     -.072159    .1224006
         ew_ledig |    .240107   .0505368     4.75   0.000     .1408621     .339352
       ew_married |   .4155908   .0527758     7.87   0.000     .3119489    .5192328
        wb_anteil |  -.2930157   .0189309   -15.48   0.000    -.3301925   -.2558389
          wb_ausl |   .0179039   .0146885     1.22   0.223    -.0109416    .0467495
         wb_18t24 |  -.0115658   .0284659    -0.41   0.685    -.0674676     .044336
         wb_25t34 |  -.0477759   .0178076    -2.68   0.007    -.0827468    -.012805
         wb_35t44 |   .0032117   .0213874     0.15   0.881    -.0387893    .0452127
         wb_45t59 |   .0153444   .0206747     0.74   0.458    -.0252569    .0559457
          avg_dur |  -.0230795   .0203066    -1.14   0.256    -.0629579    .0167988
          hh_kids |  -.0118939   .0389695    -0.31   0.760    -.0884228     .064635
mpreis_flats_rent |    .039478   .0244055     1.62   0.106      -.00845     .087406
            _cons |   16.31574   8.009025     2.04   0.042      .587488      32.044
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      18.22
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9646
                                                  Adj R-squared   =     0.9566
                                                  Within R-sq.    =     0.2661
Number of clusters (sb_new)  =        618         Root MSE        =     1.6209

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0241544   .3252739     0.07   0.941    -.6146238    .6629325
          F6event |   .2349085   .2511333     0.94   0.350    -.2582711    .7280881
          F5event |  -.4608631   .2585894    -1.78   0.075    -.9686851     .046959
          F4event |  -.2271104   .1607743    -1.41   0.158    -.5428417    .0886208
          F3event |   .0460537   .1523218     0.30   0.762    -.2530783    .3451857
          F2event |  -.0566232   .1226165    -0.46   0.644    -.2974195    .1841731
          L0event |   .2767847   .2056263     1.35   0.179    -.1270276    .6805969
          L1event |   .7137573   .2043152     3.49   0.001     .3125197    1.114995
          L2event |   .8182617   .2363258     3.46   0.001     .3541613    1.282362
          L3event |   .4109613   .2548302     1.61   0.107    -.0894785     .911401
          L4event |   1.162752   .6374885     1.82   0.069    -.0891584    2.414662
          L5event |   2.145834   .6403771     3.35   0.001     .8882505    3.403417
          L6event |   .1176547   .9933817     0.12   0.906    -1.833164    2.068474
          L7event |   .5501641   .4557637     1.21   0.228     -.344872      1.4452
        F7event_n |   -.228306   .3123393    -0.73   0.465     -.841683    .3850711
        F6event_n |   .0232749   .1961663     0.12   0.906    -.3619596    .4085094
        F5event_n |   .1145518   .2004593     0.57   0.568    -.2791135    .5082171
        F4event_n |   .1892538   .1824766     1.04   0.300    -.1690967    .5476043
        F3event_n |   .2408237   .2061764     1.17   0.243    -.1640689    .6457162
        F2event_n |    .245096   .1483814     1.65   0.099    -.0462979    .5364898
        L0event_n |  -.2608042   .1859971    -1.40   0.161    -.6260684      .10446
        L1event_n |    .027868    .163623     0.17   0.865    -.2934576    .3491936
        L2event_n |   .0372266   .2273231     0.16   0.870    -.4091943    .4836475
        L3event_n |  -.1694855   .3204208    -0.53   0.597    -.7987331     .459762
        L4event_n |    -.40009   .4928367    -0.81   0.417    -1.367931    .5677507
        L5event_n |  -.2711024   .5396576    -0.50   0.616    -1.330891    .7886859
        L6event_n |   .8677324   .7623243     1.14   0.255    -.6293325    2.364797
        L7event_n |   1.320426   .5037794     2.62   0.009     .3310961    2.309756
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |   2.576106   .2493177    10.33   0.000     2.086491     3.06572
        ln_ew_ges |   3.315098   1.343958     2.47   0.014     .6758124    5.954384
         ew_biodt |   .3906062   .0284989    13.71   0.000     .3346397    .4465728
        ew_dtmihi |  -.1994808   .0594625    -3.35   0.001    -.3162542   -.0827074
         ew_ledig |   .1908477   .0713719     2.67   0.008     .0506864     .331009
       ew_married |   .2213483   .0732955     3.02   0.003     .0774094    .3652872
        wb_anteil |  -.2393416   .0211496   -11.32   0.000    -.2808755   -.1978077
          wb_ausl |  -.0715474   .0139511    -5.13   0.000    -.0989447   -.0441501
         wb_18t24 |  -.0296429   .0274692    -1.08   0.281    -.0835874    .0243016
         wb_25t34 |   .0337734   .0179962     1.88   0.061    -.0015678    .0691145
         wb_35t44 |   -.006961   .0233232    -0.30   0.765    -.0527635    .0388414
         wb_45t59 |  -.0386813   .0196389    -1.97   0.049    -.0772486   -.0001141
          avg_dur |   .0418403   .0218651     1.91   0.056    -.0010987    .0847793
          hh_kids |  -.0984319   .0439492    -2.24   0.025    -.1847401   -.0121237
mpreis_flats_rent |  -.0251661   .0229321    -1.10   0.273    -.0702006    .0198684
            _cons |  -16.20358   11.10205    -1.46   0.145    -38.00596    5.598809
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      31.01
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4451
Number of clusters (sb_new)  =        618         Root MSE        =     1.6197

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0631124   .3190433    -0.20   0.843    -.6896547      .56343
          F6event |   .2634087   .2714958     0.97   0.332     -.269759    .7965765
          F5event |   -.235534   .2626605    -0.90   0.370     -.751351    .2802829
          F4event |  -.2269498   .1698964    -1.34   0.182    -.5605952    .1066955
          F3event |  -.0526821   .1584261    -0.33   0.740    -.3638019    .2584377
          F2event |  -.0328365   .1319091    -0.25   0.803    -.2918817    .2262087
          L0event |  -.2899549   .1620392    -1.79   0.074    -.6081701    .0282603
          L1event |   .1000626   .2025837     0.49   0.622    -.2977745    .4978997
          L2event |   .3685882   .2307519     1.60   0.111     -.084566    .8217425
          L3event |  -.0160642   .2382535    -0.07   0.946    -.4839502    .4518219
          L4event |   .6887712   .6940676     0.99   0.321      -.67425    2.051792
          L5event |   2.280651   1.002516     2.27   0.023     .3118935    4.249408
          L6event |   .6417498   1.145219     0.56   0.575    -1.607249    2.890749
          L7event |   1.519451   .8028953     1.89   0.059    -.0572882     3.09619
        F7event_n |   .3524632   .2530537     1.39   0.164    -.1444879    .8494142
        F6event_n |  -.0006432   .2357373    -0.00   0.998    -.4635878    .4623015
        F5event_n |   .1015661   .2163362     0.47   0.639    -.3232784    .5264106
        F4event_n |  -.0885039    .164001    -0.54   0.590    -.4105716    .2335638
        F3event_n |   .0430467   .1539903     0.28   0.780    -.2593619    .3454553
        F2event_n |   .1603611   .1100904     1.46   0.146    -.0558363    .3765585
        L0event_n |   .0026593   .1443898     0.02   0.985    -.2808958    .2862145
        L1event_n |   .1083547   .1356714     0.80   0.425     -.158079    .3747883
        L2event_n |     .00456   .2152275     0.02   0.983    -.4181072    .4272272
        L3event_n |  -.4379375   .2590765    -1.69   0.091     -.946716    .0708411
        L4event_n |  -.1188268   .4812389    -0.25   0.805    -1.063892     .826238
        L5event_n |   .3865539   .6924148     0.56   0.577    -.9732216    1.746329
        L6event_n |  -.2205575    .793401    -0.28   0.781    -1.778651    1.337536
        L7event_n |   1.579312   1.024817     1.54   0.124    -.4332408    3.591865
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -.7896896   .2198725    -3.59   0.000    -1.221479   -.3579005
        ln_ew_ges |   1.512194    1.08863     1.39   0.165    -.6256751    3.650063
         ew_biodt |   .7622942   .0317166    24.03   0.000     .7000087    .8245797
        ew_dtmihi |  -.1743598   .0529244    -3.29   0.001    -.2782936    -.070426
         ew_ledig |   .4309549   .0718103     6.00   0.000     .2899327    .5719771
       ew_married |   .6369392   .0700486     9.09   0.000     .4993766    .7745019
        wb_anteil |  -.5323573   .0240683   -22.12   0.000    -.5796231   -.4850916
          wb_ausl |  -.0536434   .0175201    -3.06   0.002    -.0880497   -.0192371
         wb_18t24 |  -.0412087   .0255469    -1.61   0.107     -.091378    .0089606
         wb_25t34 |  -.0140025   .0168625    -0.83   0.407    -.0471173    .0191123
         wb_35t44 |  -.0037493   .0210129    -0.18   0.858    -.0450147    .0375161
         wb_45t59 |  -.0233369   .0196962    -1.18   0.237    -.0620167    .0153429
          avg_dur |   .0187608   .0222989     0.84   0.400    -.0250302    .0625518
          hh_kids |  -.1103259   .0377352    -2.92   0.004    -.1844308    -.036221
mpreis_flats_rent |   .0143119   .0240203     0.60   0.552    -.0328596    .0614833
            _cons |   .1121542   10.05958     0.01   0.991    -19.64302    19.86733
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      19.18
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9754
                                                  Adj R-squared   =     0.9699
                                                  Within R-sq.    =     0.2710
Number of clusters (sb_new)  =        618         Root MSE        =     1.5935

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0835205   .3247341    -0.26   0.797    -.7212386    .5541975
          F6event |   .0252353   .2919654     0.09   0.931     -.548131    .5986016
          F5event |   .2183814   .2402498     0.91   0.364    -.2534251    .6901879
          F4event |  -.0003902    .167626    -0.00   0.998    -.3295768    .3287964
          F3event |  -.1077444   .1696262    -0.64   0.526     -.440859    .2253702
          F2event |   .0266064   .1216276     0.22   0.827    -.2122478    .2654606
          L0event |   -.561129   .2063842    -2.72   0.007    -.9664298   -.1558283
          L1event |   -.635923   .1995988    -3.19   0.002    -1.027898   -.2439476
          L2event |  -.4697401   .2300443    -2.04   0.042    -.9215049   -.0179753
          L3event |  -.3994508   .2473124    -1.62   0.107    -.8851268    .0862253
          L4event |   -.743745   .4075844    -1.82   0.069    -1.544166     .056676
          L5event |  -.3858835   .7301634    -0.53   0.597     -1.81979    1.048023
          L6event |   .8024056   .5011374     1.60   0.110    -.1817363    1.786547
          L7event |    .996293   1.025529     0.97   0.332    -1.017657    3.010243
        F7event_n |     .05249   .3449794     0.15   0.879    -.6249862    .7299661
        F6event_n |   .0397454   .3005282     0.13   0.895    -.5504368    .6299276
        F5event_n |    .014799   .2096339     0.07   0.944    -.3968833    .4264814
        F4event_n |   .0649187   .1379161     0.47   0.638    -.2059231    .3357606
        F3event_n |   .0717232   .1205705     0.59   0.552    -.1650552    .3085015
        F2event_n |  -.0060866   .1050892    -0.06   0.954    -.2124624    .2002893
        L0event_n |  -.0797379   .2108404    -0.38   0.705    -.4937897     .334314
        L1event_n |    .165294   .2221667     0.74   0.457    -.2710005    .6015885
        L2event_n |   .2355639   .2012874     1.17   0.242    -.1597275    .6308552
        L3event_n |   .4454387   .2591403     1.72   0.086    -.0634654    .9543427
        L4event_n |    .516882     .83899     0.62   0.538     -1.13074    2.164504
        L5event_n |  -.0043154   1.347883    -0.00   0.997    -2.651311     2.64268
        L6event_n |   2.360848   1.027247     2.30   0.022     .3435238    4.378172
        L7event_n |  -.5096927   1.759825    -0.29   0.772    -3.965666    2.946281
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -3.416198   .2544894   -13.42   0.000    -3.915968   -2.916427
        ln_ew_ges |  -1.459036   .8418833    -1.73   0.084    -3.112341    .1942677
         ew_biodt |   .3728601   .0261109    14.28   0.000     .3215832     .424137
        ew_dtmihi |   .0342352   .0487281     0.70   0.483    -.0614579    .1299283
         ew_ledig |    .237901   .0499472     4.76   0.000     .1398139    .3359882
       ew_married |   .4164416   .0524192     7.94   0.000     .3134998    .5193833
        wb_anteil |  -.2904402   .0189332   -15.34   0.000    -.3276215   -.2532589
          wb_ausl |   .0179234   .0147945     1.21   0.226    -.0111302     .046977
         wb_18t24 |  -.0078405   .0281898    -0.28   0.781       -.0632    .0475191
         wb_25t34 |  -.0500859   .0176417    -2.84   0.005    -.0847309    -.015441
         wb_35t44 |   .0018011   .0211177     0.09   0.932    -.0396701    .0432723
         wb_45t59 |   .0155797   .0209594     0.74   0.458    -.0255808    .0567402
          avg_dur |  -.0224302    .020352    -1.10   0.271    -.0623978    .0175374
          hh_kids |  -.0266895   .0382826    -0.70   0.486    -.1018696    .0484906
mpreis_flats_rent |   .0365355   .0240833     1.52   0.130    -.0107597    .0838306
            _cons |   13.65072   7.887662     1.73   0.084    -1.839202    29.14064
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      17.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9645
                                                  Adj R-squared   =     0.9565
                                                  Within R-sq.    =     0.2649
Number of clusters (sb_new)  =        618         Root MSE        =     1.6222

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0213355    .319749     0.07   0.947    -.6065928    .6492638
          F6event |   .2213736   .2470286     0.90   0.371    -.2637453    .7064924
          F5event |  -.4253344   .2471999    -1.72   0.086    -.9107896    .0601207
          F4event |  -.2293352    .156666    -1.46   0.144    -.5369983     .078328
          F3event |   .0420244    .152481     0.28   0.783    -.2574203    .3414692
          F2event |   -.054388     .12206    -0.45   0.656    -.2940914    .1853154
          L0event |   .2483839   .2015611     1.23   0.218     -.147445    .6442128
          L1event |    .712751   .2033807     3.50   0.000     .3133488    1.112153
          L2event |   .8257149   .2372535     3.48   0.001     .3597926    1.291637
          L3event |   .4869041   .2433378     2.00   0.046     .0090334    .9647748
          L4event |    1.34749   .7599666     1.77   0.077    -.1449446    2.839925
          L5event |   2.346699   .7738844     3.03   0.003     .8269321    3.866466
          L6event |   .0063267   .6885633     0.01   0.993    -1.345885    1.358539
          L7event |   .1329528   .4933583     0.27   0.788    -.8359122    1.101818
        F7event_n |  -.3503531   .2630912    -1.33   0.183    -.8670158    .1663097
        F6event_n |  -.2130259   .2287148    -0.93   0.352    -.6621797     .236128
        F5event_n |  -.3083835   .2345721    -1.31   0.189    -.7690401    .1522731
        F4event_n |  -.1131028   .1420941    -0.80   0.426    -.3921495    .1659438
        F3event_n |  -.0929941    .120938    -0.77   0.442    -.3304941    .1445059
        F2event_n |  -.1312622   .0941995    -1.39   0.164    -.3162527    .0537284
        L0event_n |   .2240001   .1739215     1.29   0.198    -.1175498    .5655501
        L1event_n |  -.1236693   .1665838    -0.74   0.458    -.4508093    .2034707
        L2event_n |  -.1882371   .1851771    -1.02   0.310    -.5518909    .1754166
        L3event_n |  -.2221689   .2869543    -0.77   0.439    -.7856945    .3413567
        L4event_n |  -.0134072   1.145669    -0.01   0.991     -2.26329    2.236476
        L5event_n |  -.1589195   1.199575    -0.13   0.895    -2.514665    2.196826
        L6event_n |  -2.471028   .9056841    -2.73   0.007    -4.249626   -.6924311
        L7event_n |  -2.501965   .7747945    -3.23   0.001    -4.023519   -.9804106
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |   2.618949    .255332    10.26   0.000     2.117523    3.120374
        ln_ew_ges |   3.047147   1.270679     2.40   0.017     .5517664    5.542527
         ew_biodt |   .3888456   .0279606    13.91   0.000     .3339361     .443755
        ew_dtmihi |  -.2086805    .057736    -3.61   0.000    -.3220633   -.0952977
         ew_ledig |   .1924586   .0709555     2.71   0.007     .0531151    .3318021
       ew_married |   .2163192   .0727987     2.97   0.003     .0733559    .3592825
        wb_anteil |  -.2401254   .0210778   -11.39   0.000    -.2815184   -.1987324
          wb_ausl |   -.070654   .0141504    -4.99   0.000    -.0984428   -.0428652
         wb_18t24 |  -.0360944   .0271239    -1.33   0.184    -.0893607     .017172
         wb_25t34 |   .0345479   .0178046     1.94   0.053    -.0004172    .0695129
         wb_35t44 |  -.0056039   .0232663    -0.24   0.810    -.0512945    .0400868
         wb_45t59 |  -.0404954   .0195844    -2.07   0.039    -.0789554   -.0020353
          avg_dur |   .0401912   .0222985     1.80   0.072     -.003599    .0839814
          hh_kids |  -.0848758   .0411077    -2.06   0.039    -.1656038   -.0041478
mpreis_flats_rent |  -.0224328   .0230757    -0.97   0.331    -.0677492    .0228835
            _cons |  -13.89071   10.63895    -1.31   0.192    -34.78364    7.002232
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      29.67
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4450
Number of clusters (sb_new)  =        618         Root MSE        =     1.6198

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0621852   .3092676    -0.20   0.841      -.66953    .5451596
          F6event |   .2466081   .2719173     0.91   0.365    -.2873875    .7806037
          F5event |  -.2069527   .2573601    -0.80   0.422    -.7123607    .2984553
          F4event |  -.2297251   .1692502    -1.36   0.175    -.5621015    .1026512
          F3event |  -.0657202   .1593453    -0.41   0.680    -.3786451    .2472047
          F2event |  -.0277812   .1332768    -0.21   0.835    -.2895124    .2339499
          L0event |  -.3127448   .1638474    -1.91   0.057    -.6345109    .0090213
          L1event |   .0768284   .2044563     0.38   0.707    -.3246863     .478343
          L2event |   .3559748   .2297355     1.55   0.122    -.0951835    .8071332
          L3event |   .0874536   .2328694     0.38   0.707    -.3698591    .5447663
          L4event |   .6037471   .7664939     0.79   0.431    -.9015061       2.109
          L5event |   1.960814    1.31354     1.49   0.136    -.6187374    4.540366
          L6event |   .8087309   .8659467     0.93   0.351    -.8918294    2.509291
          L7event |   1.129246   .8621597     1.31   0.191    -.5638768     2.82237
        F7event_n |  -.2978635   .3384702    -0.88   0.379    -.9625567    .3668297
        F6event_n |   -.173281    .282208    -0.61   0.539    -.7274858    .3809237
        F5event_n |  -.2935841    .207222    -1.42   0.157      -.70053    .1133618
        F4event_n |  -.0481838   .1451421    -0.33   0.740    -.3332163    .2368487
        F3event_n |   -.021271   .1131414    -0.19   0.851    -.2434599    .2009179
        F2event_n |  -.1373484   .0986017    -1.39   0.164    -.3309841    .0562872
        L0event_n |   .1442624   .1473903     0.98   0.328     -.145185    .4337098
        L1event_n |   .0416247   .1764527     0.24   0.814    -.3048959    .3881454
        L2event_n |   .0473274   .1574189     0.30   0.764    -.2618144    .3564692
        L3event_n |   .2232708    .267871     0.83   0.405    -.3027786    .7493202
        L4event_n |   .5034704    1.00975     0.50   0.618    -1.479492    2.486433
        L5event_n |  -.1632327   2.038636    -0.08   0.936     -4.16674    3.840274
        L6event_n |  -.1101817   1.135057    -0.10   0.923    -2.339225    2.118861
        L7event_n |  -3.011654    1.59294    -1.89   0.059    -6.139895    .1165873
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -.7972492   .2263646    -3.52   0.000    -1.241788   -.3527106
        ln_ew_ges |    1.58811   1.063524     1.49   0.136    -.5004558    3.676677
         ew_biodt |   .7617057    .031509    24.17   0.000     .6998279    .8235835
        ew_dtmihi |  -.1744452   .0519971    -3.35   0.001    -.2765579   -.0723324
         ew_ledig |   .4303598   .0714509     6.02   0.000     .2900434    .5706761
       ew_married |   .6327609   .0695338     9.10   0.000     .4962093    .7693124
        wb_anteil |  -.5305656   .0239947   -22.11   0.000    -.5776867   -.4834445
          wb_ausl |  -.0527306   .0174376    -3.02   0.003    -.0869748   -.0184864
         wb_18t24 |  -.0439348   .0256722    -1.71   0.088    -.0943504    .0064807
         wb_25t34 |  -.0155381   .0166736    -0.93   0.352     -.048282    .0172058
         wb_35t44 |  -.0038027   .0211351    -0.18   0.857    -.0453082    .0377027
         wb_45t59 |  -.0249157   .0197321    -1.26   0.207    -.0636659    .0138345
          avg_dur |    .017761   .0222188     0.80   0.424    -.0258725    .0613946
          hh_kids |  -.1115654   .0355225    -3.14   0.002     -.181325   -.0418059
mpreis_flats_rent |   .0141027   .0236738     0.60   0.552    -.0323883    .0605936
            _cons |  -.2400017   9.946368    -0.02   0.981    -19.77284    19.29284
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      18.33
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9755
                                                  Adj R-squared   =     0.9700
                                                  Within R-sq.    =     0.2746
Number of clusters (sb_new)  =        618         Root MSE        =     1.5895

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0349327   .3260545    -0.11   0.915     -.675244    .6053785
          F6event |   .0591327    .285138     0.21   0.836    -.5008259    .6190913
          F5event |   .2443161   .2399639     1.02   0.309    -.2269289    .7155611
          F4event |   .0297025   .1681376     0.18   0.860    -.3004888    .3598937
          F3event |  -.0804931   .1684664    -0.48   0.633    -.4113301    .2503439
          F2event |   .0216399   .1194189     0.18   0.856    -.2128769    .2561567
          L0event |  -.5705453   .2063235    -2.77   0.006    -.9757267    -.165364
          L1event |  -.6268268   .2015206    -3.11   0.002    -1.022576   -.2310774
          L2event |  -.4295307   .2263642    -1.90   0.058    -.8740684    .0150071
          L3event |  -.3628884   .2422648    -1.50   0.135     -.838652    .1128752
          L4event |  -.2623786   .4101467    -0.64   0.523    -1.067831    .5430741
          L5event |   .2103542   .4630614     0.45   0.650    -.6990134    1.119722
          L6event |   .5606252   .5430033     1.03   0.302    -.5057336    1.626984
          L7event |    1.80343   1.337375     1.35   0.178    -.8229294    4.429788
        F7event_n |    .138775   .3448567     0.40   0.688    -.5384602    .8160102
        F6event_n |   .1192883   .2819995     0.42   0.672    -.4345069    .6730834
        F5event_n |   .1719799   .2373268     0.72   0.469    -.2940863    .6380461
        F4event_n |    -.13689   .1785964    -0.77   0.444    -.4876205    .2138404
        F3event_n |   .0360392   .1899232     0.19   0.850    -.3369351    .4090134
        F2event_n |  -.0155635   .1377104    -0.11   0.910    -.2860013    .2548744
        L0event_n |   .4995568   .1988542     2.51   0.012     .1090437    .8900699
        L1event_n |    .161167   .1507504     1.07   0.285    -.1348791    .4572132
        L2event_n |   .2271681   .2065834     1.10   0.272    -.1785238      .63286
        L3event_n |    .060279   .2295423     0.26   0.793    -.3904998    .5110579
        L4event_n |   .7331716   .4492474     1.63   0.103    -.1490677    1.615411
        L5event_n |    1.20211   .6132319     1.96   0.050    -.0021651    2.406385
        L6event_n |  -1.085268    .757998    -1.43   0.153    -2.573837    .4033006
        L7event_n |   2.016008   1.311982     1.54   0.125    -.5604827    4.592499
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -3.326776   .2486637   -13.38   0.000    -3.815106   -2.838446
        ln_ew_ges |  -1.768267     .85755    -2.06   0.040    -3.452338   -.0841969
         ew_biodt |   .3716059   .0262964    14.13   0.000     .3199646    .4232472
        ew_dtmihi |    .026711   .0495245     0.54   0.590    -.0705459     .123968
         ew_ledig |   .2461755   .0504138     4.88   0.000     .1471721    .3451789
       ew_married |   .4216808   .0528642     7.98   0.000     .3178653    .5254964
        wb_anteil |  -.2918289   .0190723   -15.30   0.000    -.3292834   -.2543744
          wb_ausl |    .018688   .0149994     1.25   0.213     -.010768     .048144
         wb_18t24 |  -.0081021    .028714    -0.28   0.778     -.064491    .0482868
         wb_25t34 |  -.0494581     .01795    -2.76   0.006    -.0847086   -.0142077
         wb_35t44 |   .0016673   .0213531     0.08   0.938    -.0402664    .0436009
         wb_45t59 |   .0154249   .0208209     0.74   0.459    -.0254634    .0563133
          avg_dur |  -.0195161   .0203793    -0.96   0.339    -.0595374    .0205051
          hh_kids |  -.0144371   .0387119    -0.37   0.709    -.0904602     .061586
mpreis_flats_rent |   .0384069   .0239079     1.61   0.109    -.0085438    .0853576
            _cons |   15.45017     7.9225     1.95   0.052    -.1081682     31.0085
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      18.78
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9649
                                                  Adj R-squared   =     0.9570
                                                  Within R-sq.    =     0.2735
Number of clusters (sb_new)  =        618         Root MSE        =     1.6127

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0869825   .3167397    -0.27   0.784    -.7090011    .5350361
          F6event |    .143576   .2482564     0.58   0.563    -.3439539    .6311058
          F5event |  -.5461179   .2522936    -2.16   0.031    -1.041576   -.0506596
          F4event |  -.2798161   .1568014    -1.78   0.075    -.5877451     .028113
          F3event |   .0110117   .1510611     0.07   0.942    -.2856445    .3076679
          F2event |  -.0685987   .1213364    -0.57   0.572     -.306881    .1696837
          L0event |   .2991167   .2042846     1.46   0.144    -.1020607    .7002941
          L1event |   .7123195   .2038748     3.49   0.001      .311947    1.112692
          L2event |   .7763634   .2348964     3.31   0.001     .3150701    1.237657
          L3event |   .3663088   .2363248     1.55   0.122    -.0977896    .8304072
          L4event |   1.249164   .5080593     2.46   0.014     .2514293      2.2469
          L5event |   2.183196   .6100101     3.58   0.000     .9852485    3.381144
          L6event |   .6133798   .5362926     1.14   0.253    -.4398003     1.66656
          L7event |   .3768169   .6098848     0.62   0.537    -.8208848    1.574519
        F7event_n |   .4066508    .263372     1.54   0.123    -.1105635    .9238651
        F6event_n |   .0990943   .1918521     0.52   0.606     -.277668    .4758567
        F5event_n |   .3052947   .2157098     1.42   0.157    -.1183198    .7289092
        F4event_n |   .2594959   .1922569     1.35   0.178    -.1180613    .6370531
        F3event_n |   .1638084   .1963678     0.83   0.404    -.2218218    .5494386
        F2event_n |   .3818846   .1369352     2.79   0.005     .1129691    .6508002
        L0event_n |  -.4632889   .1709021    -2.71   0.007    -.7989091   -.1276687
        L1event_n |  -.3474765   .1879383    -1.85   0.065    -.7165527    .0215998
        L2event_n |  -.4842135   .2172537    -2.23   0.026    -.9108598   -.0575672
        L3event_n |  -.6513134   .2453044    -2.66   0.008    -1.133046   -.1695806
        L4event_n |  -.4678026   .4921528    -0.95   0.342      -1.4343    .4986951
        L5event_n |  -.5540259   .6750354    -0.82   0.412    -1.879672    .7716196
        L6event_n |   1.550752   .7172866     2.16   0.031     .1421332    2.959371
        L7event_n |   .8543036    .598288     1.43   0.154    -.3206241    2.029231
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |   2.472079    .251555     9.83   0.000     1.978071    2.966086
        ln_ew_ges |   3.385349   1.315781     2.57   0.010      .801397    5.969302
         ew_biodt |   .3937747    .027914    14.11   0.000     .3389567    .4485927
        ew_dtmihi |  -.1895295   .0588873    -3.22   0.001    -.3051733   -.0738857
         ew_ledig |   .1830104   .0693579     2.64   0.009     .0468043    .3192166
       ew_married |   .2134833   .0714656     2.99   0.003      .073138    .3538286
        wb_anteil |  -.2437526   .0208843   -11.67   0.000    -.2847655   -.2027398
          wb_ausl |  -.0699094   .0140423    -4.98   0.000    -.0974859    -.042333
         wb_18t24 |  -.0342376   .0273138    -1.25   0.211    -.0878768    .0194016
         wb_25t34 |   .0325517    .017865     1.82   0.069    -.0025319    .0676353
         wb_35t44 |  -.0016715   .0232159    -0.07   0.943    -.0472632    .0439203
         wb_45t59 |  -.0384756   .0195779    -1.97   0.050    -.0769229   -.0000283
          avg_dur |   .0361918   .0216931     1.67   0.096    -.0064096    .0787931
          hh_kids |   -.113277   .0431218    -2.63   0.009    -.1979603   -.0285937
mpreis_flats_rent |  -.0240046   .0225876    -1.06   0.288    -.0683624    .0203533
            _cons |   -15.8439   10.90218    -1.45   0.147    -37.25379    5.565989
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      32.26
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9905
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4482
Number of clusters (sb_new)  =        618         Root MSE        =     1.6150

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1219154   .3093257    -0.39   0.694    -.7293742    .4855434
          F6event |   .2027079   .2648302     0.77   0.444      -.31737    .7227858
          F5event |  -.3018014    .255251    -1.18   0.238    -.8030674    .1994646
          F4event |  -.2501132   .1680675    -1.49   0.137     -.580167    .0799405
          F3event |  -.0694817   .1571366    -0.44   0.659    -.3780692    .2391058
          F2event |  -.0469583   .1308185    -0.36   0.720    -.3038618    .2099451
          L0event |  -.2714283    .161695    -1.68   0.094    -.5889675     .046111
          L1event |   .0854931    .203703     0.42   0.675    -.3145423    .4855284
          L2event |    .346833   .2319506     1.50   0.135    -.1086755    .8023414
          L3event |   .0034207   .2274265     0.02   0.988    -.4432031    .4500445
          L4event |   .9867852   .5978849     1.65   0.099     -.187351    2.160921
          L5event |   2.393549    .858588     2.79   0.005     .7074398    4.079658
          L6event |   1.174003   .6726107     1.75   0.081    -.1468809    2.494887
          L7event |   2.180246   .9996219     2.18   0.030     .2171721     4.14332
        F7event_n |   .5454267   .2866322     1.90   0.058    -.0174664     1.10832
        F6event_n |   .2183826   .2539505     0.86   0.390    -.2803295    .7170946
        F5event_n |   .4772741   .2345753     2.03   0.042     .0166114    .9379368
        F4event_n |   .1226058   .2102141     0.58   0.560    -.2902161    .5354277
        F3event_n |   .1998471   .1683046     1.19   0.236    -.1306722    .5303665
        F2event_n |   .3663211   .1178783     3.11   0.002     .1348297    .5978125
        L0event_n |   .0362677   .1627803     0.22   0.824    -.2834029    .3559383
        L1event_n |  -.1863094    .187811    -0.99   0.322    -.5551357     .182517
        L2event_n |  -.2570453   .2258116    -1.14   0.255    -.7004977    .1864071
        L3event_n |  -.5910347   .2043017    -2.89   0.004    -.9922456   -.1898237
        L4event_n |    .265367   .4720701     0.56   0.574     -.661692    1.192426
        L5event_n |   .6480812   .9347794     0.69   0.488    -1.187654    2.483816
        L6event_n |   .4654833   .9195406     0.51   0.613    -1.340326    2.271292
        L7event_n |   2.870308   1.145761     2.51   0.012     .6202453    5.120372
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -.8546974   .2230296    -3.83   0.000    -1.292687   -.4167082
        ln_ew_ges |   1.617082   1.076244     1.50   0.133    -.4964636    3.730628
         ew_biodt |   .7653806   .0312339    24.50   0.000     .7040429    .8267183
        ew_dtmihi |  -.1628183   .0525343    -3.10   0.002     -.265986   -.0596506
         ew_ledig |   .4291861   .0708162     6.06   0.000     .2901161    .5682561
       ew_married |   .6351642   .0689772     9.21   0.000     .4997056    .7706228
        wb_anteil |  -.5355815   .0238022   -22.50   0.000    -.5823247   -.4888384
          wb_ausl |  -.0512214   .0177534    -2.89   0.004    -.0860859   -.0163569
         wb_18t24 |  -.0423397   .0254501    -1.66   0.097    -.0923191    .0076397
         wb_25t34 |  -.0169064   .0168229    -1.00   0.315    -.0499435    .0161306
         wb_35t44 |  -4.17e-06   .0209654    -0.00   1.000    -.0411763     .041168
         wb_45t59 |  -.0230506   .0193841    -1.19   0.235    -.0611173    .0150161
          avg_dur |   .0166756   .0220939     0.75   0.451    -.0267128     .060064
          hh_kids |  -.1277142    .036642    -3.49   0.001    -.1996723   -.0557561
mpreis_flats_rent |   .0144023   .0237795     0.61   0.545    -.0322963     .061101
            _cons |  -.3937465   9.951699    -0.04   0.968    -19.93706    19.14956
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      20.51
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9755
                                                  Adj R-squared   =     0.9700
                                                  Within R-sq.    =     0.2747
Number of clusters (sb_new)  =        618         Root MSE        =     1.5894

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1516144   .3416863    -0.44   0.657    -.8226234    .5193947
          F6event |   .0095181   .2947862     0.03   0.974    -.5693879     .588424
          F5event |   .2257162   .2407356     0.94   0.349    -.2470442    .6984767
          F4event |    .015082   .1691883     0.09   0.929    -.3171728    .3473367
          F3event |  -.0864239   .1685177    -0.51   0.608    -.4173616    .2445139
          F2event |   .0329185   .1207333     0.27   0.785    -.2041796    .2700166
          L0event |  -.6119196   .2087089    -2.93   0.003    -1.021786   -.2020538
          L1event |  -.6322212   .2014225    -3.14   0.002    -1.027778   -.2366644
          L2event |  -.4286642   .2290331    -1.87   0.062    -.8784432    .0211147
          L3event |  -.3374791    .244953    -1.38   0.169    -.8185217    .1435635
          L4event |  -.7036778   .3927342    -1.79   0.074    -1.474936    .0675799
          L5event |  -.8100419   .5658379    -1.43   0.153    -1.921243    .3011597
          L6event |   .7753141   .5022732     1.54   0.123    -.2110582    1.761686
          L7event |   .5161774   .8303332     0.62   0.534    -1.114444    2.146799
        F7event_n |  -.0086903   .2706602    -0.03   0.974    -.5402172    .5228367
        F6event_n |   .2014344   .2497329     0.81   0.420    -.2889952     .691864
        F5event_n |    .379387   .1926439     1.97   0.049     .0010699    .7577042
        F4event_n |   .3357159   .1571042     2.14   0.033     .0271922    .6442396
        F3event_n |   .2690805   .1601856     1.68   0.094    -.0454946    .5836556
        F2event_n |   .1776907   .1151638     1.54   0.123    -.0484698    .4038513
        L0event_n |  -.1281873   .1786758    -0.72   0.473    -.4790738    .2226992
        L1event_n |  -.3467684   .1703818    -2.04   0.042     -.681367   -.0121697
        L2event_n |  -.2615213   .2030649    -1.29   0.198    -.6603034    .1372609
        L3event_n |  -.2115876   .2243299    -0.94   0.346    -.6521304    .2289552
        L4event_n |  -.5289304   .3165974    -1.67   0.095    -1.150669    .0928087
        L5event_n |  -.7386078    .674103    -1.10   0.274    -2.062422    .5852065
        L6event_n |  -1.582424   .6280912    -2.52   0.012     -2.81588   -.3489685
        L7event_n |  -1.035876   1.574432    -0.66   0.511    -4.127771    2.056019
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -3.344765   .2452112   -13.64   0.000    -3.826315   -2.863216
        ln_ew_ges |  -1.695208   .8875518    -1.91   0.057    -3.438196    .0477808
         ew_biodt |   .3669252   .0261523    14.03   0.000     .3155668    .4182836
        ew_dtmihi |   .0385889   .0483956     0.80   0.426    -.0564513     .133629
         ew_ledig |   .2249755    .049444     4.55   0.000     .1278766    .3220744
       ew_married |   .4057925   .0523703     7.75   0.000     .3029469    .5086382
        wb_anteil |   -.283282   .0191629   -14.78   0.000    -.3209144   -.2456496
          wb_ausl |   .0159184   .0150671     1.06   0.291    -.0136707    .0455075
         wb_18t24 |  -.0079606   .0276952    -0.29   0.774     -.062349    .0464277
         wb_25t34 |  -.0449382   .0175868    -2.56   0.011    -.0794754    -.010401
         wb_35t44 |   .0054152   .0212982     0.25   0.799    -.0364105     .047241
         wb_45t59 |   .0191325   .0202502     0.94   0.345    -.0206353    .0589003
          avg_dur |  -.0476364   .0205299    -2.32   0.021    -.0879533   -.0073195
          hh_kids |  -.0248746   .0373684    -0.67   0.506    -.0982593      .04851
mpreis_flats_rent |   .0353481   .0239408     1.48   0.140    -.0116672    .0823633
            _cons |   16.68213   7.961069     2.10   0.037     1.048048     32.3162
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      17.10
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9646
                                                  Adj R-squared   =     0.9566
                                                  Within R-sq.    =     0.2669
Number of clusters (sb_new)  =        618         Root MSE        =     1.6200

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0256868   .3470978     0.07   0.941    -.6559496    .7073232
          F6event |   .2297593   .2534814     0.91   0.365    -.2680316    .7275503
          F5event |  -.4497622   .2576033    -1.75   0.081    -.9556478    .0561234
          F4event |  -.2356869   .1581925    -1.49   0.137    -.5463479    .0749741
          F3event |   .0213567   .1513428     0.14   0.888    -.2758527    .3185661
          F2event |  -.0446567   .1225564    -0.36   0.716    -.2853349    .1960215
          L0event |    .253904   .2045876     1.24   0.215    -.1478684    .6556764
          L1event |   .6791338   .2048518     3.32   0.001     .2768424    1.081425
          L2event |   .7825744   .2337189     3.35   0.001     .3235934    1.241555
          L3event |   .4844365   .2409711     2.01   0.045     .0112135    .9576596
          L4event |   1.253841   .7605902     1.65   0.100    -.2398189      2.7475
          L5event |    1.32871    .514998     2.58   0.010     .3173481    2.340071
          L6event |  -.7629558   1.061293    -0.72   0.472    -2.847141    1.321229
          L7event |  -.0443604   .6358661    -0.07   0.944    -1.293084    1.204364
        F7event_n |   .1153838   .2982394     0.39   0.699    -.4703036    .7010712
        F6event_n |    .120037   .1933278     0.62   0.535    -.2596233    .4996972
        F5event_n |   .1755098   .2248293     0.78   0.435    -.2660137    .6170333
        F4event_n |   .0461828   .1489572     0.31   0.757    -.2463417    .3387073
        F3event_n |  -.0896293   .1412303    -0.63   0.526    -.3669797    .1877211
        F2event_n |   .3271874   .0994448     3.29   0.001      .131896    .5224788
        L0event_n |  -.2491632   .1755652    -1.42   0.156    -.5939409    .0956146
        L1event_n |  -.0409494   .1828422    -0.22   0.823    -.4000179    .3181192
        L2event_n |  -.4195041   .2158653    -1.94   0.052    -.8434239    .0044158
        L3event_n |   .0706245   .2463907     0.29   0.774    -.4132417    .5544906
        L4event_n |  -.3161168   .5752765    -0.55   0.583    -1.445854    .8136206
        L5event_n |  -1.069238   .3728698    -2.87   0.004    -1.801486   -.3369901
        L6event_n |  -.4771783   1.123173    -0.42   0.671    -2.682883    1.728526
        L7event_n |   1.049257   .6208045     1.69   0.092    -.1698889    2.268403
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |   2.636313   .2495396    10.56   0.000     2.146263    3.126363
        ln_ew_ges |   2.818219   1.247102     2.26   0.024     .3691399    5.267298
         ew_biodt |   .3815438   .0280165    13.62   0.000     .3265244    .4365631
        ew_dtmihi |  -.2098033   .0586845    -3.58   0.000    -.3250488   -.0945578
         ew_ledig |   .1791022   .0708872     2.53   0.012     .0398927    .3183117
       ew_married |   .2068884   .0730067     2.83   0.005     .0635166    .3502601
        wb_anteil |  -.2343028   .0213211   -10.99   0.000    -.2761736   -.1924321
          wb_ausl |   -.070528   .0141921    -4.97   0.000    -.0983986   -.0426574
         wb_18t24 |  -.0332486   .0272126    -1.22   0.222    -.0866892     .020192
         wb_25t34 |   .0362716   .0181857     1.99   0.047     .0005582     .071985
         wb_35t44 |  -.0064822   .0233566    -0.28   0.781    -.0523503    .0393858
         wb_45t59 |  -.0406602   .0195935    -2.08   0.038    -.0791382   -.0021822
          avg_dur |   .0265695   .0241922     1.10   0.273    -.0209395    .0740785
          hh_kids |  -.0780882    .040936    -1.91   0.057    -.1584791    .0023026
mpreis_flats_rent |  -.0256257   .0226094    -1.13   0.257    -.0700264     .018775
            _cons |  -10.82548   10.50741    -1.03   0.303    -31.46011    9.809147
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity
note: F1event_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  43,    617) =      35.90
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9906
                                                  Adj R-squared   =     0.9885
                                                  Within R-sq.    =     0.4542
Number of clusters (sb_new)  =        618         Root MSE        =     1.6063

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1259278   .3015865    -0.42   0.676    -.7181883    .4663327
          F6event |   .2392767   .2664845     0.90   0.370      -.28405    .7626033
          F5event |  -.2240456   .2552904    -0.88   0.380     -.725389    .2772979
          F4event |  -.2206046   .1686887    -1.31   0.191    -.5518782     .110669
          F3event |  -.0650674   .1584867    -0.41   0.682    -.3763061    .2461713
          F2event |  -.0117377   .1348823    -0.09   0.931    -.2766218    .2531464
          L0event |  -.3580153   .1630228    -2.20   0.028    -.6781621   -.0378684
          L1event |   .0469129   .2042958     0.23   0.818    -.3542866    .4481124
          L2event |   .3539104   .2226102     1.59   0.112    -.0832551    .7910759
          L3event |   .1469578   .2388324     0.62   0.539    -.3220651    .6159808
          L4event |   .5501638   .7807664     0.70   0.481     -.983118    2.083446
          L5event |   .5186674   .6519842     0.80   0.427    -.7617097    1.799044
          L6event |   .0123578   .8433509     0.01   0.988    -1.643828    1.668544
          L7event |   .4718177   1.043426     0.45   0.651    -1.577279    2.520915
        F7event_n |   .1066933   .2544721     0.42   0.675    -.3930433    .6064298
        F6event_n |   .3214715   .2502141     1.28   0.199     -.169903    .8128459
        F5event_n |   .5548968   .2369032     2.34   0.019     .0896625    1.020131
        F4event_n |   .3818988   .1917645     1.99   0.047     .0053085     .758489
        F3event_n |   .1794512   .1549556     1.16   0.247    -.1248532    .4837556
        F2event_n |    .504878   .1303078     3.87   0.000     .2489775    .7607786
        L0event_n |  -.3773504   .1621184    -2.33   0.020    -.6957212   -.0589797
        L1event_n |  -.3877177   .1991332    -1.95   0.052    -.7787787    .0033433
        L2event_n |  -.6810253   .2111542    -3.23   0.001    -1.095693   -.2663574
        L3event_n |  -.1409629   .2228424    -0.63   0.527    -.5785844    .2966586
        L4event_n |  -.8450458   .5672371    -1.49   0.137    -1.958995    .2689037
        L5event_n |  -1.807846   .5391191    -3.35   0.001    -2.866577   -.7491149
        L6event_n |  -2.059599   .7611472    -2.71   0.007    -3.554352   -.5648459
        L7event_n |   .0133791   1.526475     0.01   0.993    -2.984338    3.011096
          F1event |          0  (omitted)
        F1event_n |          0  (omitted)
   ln_street_dist |  -.7084518   .2097735    -3.38   0.001    -1.120408   -.2964952
        ln_ew_ges |   1.123011   .9815163     1.14   0.253    -.8045063    3.050529
         ew_biodt |    .748469   .0310719    24.09   0.000     .6874496    .8094885
        ew_dtmihi |  -.1712143   .0520663    -3.29   0.001     -.273463   -.0689656
         ew_ledig |   .4040778   .0698179     5.79   0.000     .2669682    .5411875
       ew_married |    .612681   .0684163     8.96   0.000      .478324     .747038
        wb_anteil |  -.5175849   .0238479   -21.70   0.000    -.5644178   -.4707519
          wb_ausl |  -.0546096   .0174181    -3.14   0.002    -.0888156   -.0204036
         wb_18t24 |  -.0412092   .0246807    -1.67   0.095    -.0896776    .0072591
         wb_25t34 |  -.0086666   .0167594    -0.52   0.605    -.0415789    .0242458
         wb_35t44 |  -.0010669   .0208918    -0.05   0.959    -.0420946    .0399607
         wb_45t59 |  -.0215277   .0192132    -1.12   0.263    -.0592589    .0162035
          avg_dur |   -.021067   .0232776    -0.91   0.366    -.0667799     .024646
          hh_kids |   -.102963   .0342361    -3.01   0.003    -.1701964   -.0357296
mpreis_flats_rent |   .0097224   .0232745     0.42   0.676    -.0359845    .0554292
            _cons |   5.856629   9.324062     0.63   0.530    -12.45412    24.16737
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         * PLOT: FIGURE D13. Effect Heterogeneity by Precinct Characteristics Conditional on Dist
> ance
.         grc1leg z_wb_60plus_urne_pos            z_wb_60plus_tot_req z_wb_60plus, leg(z_wb_60plus
> ) pos(12) col(2) imargins(small) xcommon ycommon name(gr_z_wb_60plus, replace) ///
>         title("{bf:Panel A.} Heterogeneity by % Electorate Aged 60+"    ,nobox span bexpand just
> ification(left) size(small))

.         gr_edit .plotregion1.graph3.draw_view.setstyle, style(no)               // erase extra p
> lot

.         grc1leg z_wb_18t24_urne_pos             z_wb_18t24_tot_req, row(1) imargins(small) xcomm
> on ycommon name(gr_z_wb_18t24, replace) ///
>         title("{bf:Panel B.} Heterogeneity by % Electorate Aged 18-24"  ,nobox span bexpand just
> ification(left) size(small))    

.         grc1leg z_hh_kids_urne_pos              z_hh_kids_tot_req, row(1)               imargins
> (small) xcommon ycommon name(gr_z_hh_kids, replace) ///
>         title("{bf:Panel C.} Heterogeneity by % Households with Children",nobox span bexpand jus
> tification(left) size(small))   

.         grc1leg z_mpreis_rent_urne_pos  z_mpreis_rent_tot_req, row(1)   imargins(small) xcommon 
> ycommon name(gr_z_mpreis_rent, replace) ///
>         title("{bf:Panel D.} Heterogeneity by Average Quoted Rent per sqm",nobox span bexpand ju
> stification(left) size(small)) 

.         grc1leg z_ew_dtmihi_urne_pos            z_ew_dtmihi_tot_req, row(1)     imargins(small) 
> xcommon ycommon name(gr_z_ew_dtmihi, replace) ///
>         title("{bf:Panel E.} Heterogeneity by % Germans with Migrant Background",nobox span bexp
> and justification(left) size(small)) 

.         
.         grc1leg gr_z_wb_60plus gr_z_wb_18t24 gr_z_hh_kids gr_z_mpreis_rent gr_z_ew_dtmihi , col(
> 1) pos(12) imargins(zero)

.         gr_edit .legend.Edit , style(rows(1)) style(cols(0)) keepstyles 

.         gr_edit .legend.title.DragBy -3.1 -12.6 

.         gr_edit .style.editstyle declared_ysize(8.4) editcopy                   

.         graph export "$figures/Figure_D13_ES_het_z_charac_top5_ln_dist.pdf", replace            
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D13_ES
    > _het_z_charac_top5_ln_dist.pdf saved as PDF format

.                         
. 
end of do-file
Running: 04a_rob_het_by_distance_figures_d8_d10.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output: Figure D.8, D.10 
> 
> Tasks: Heterogeneity by distance increase/decrease, 4 bins
>          
> */      
. 
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
. ********************************************************************************
. *        Prep Estimation *
. ********************************************************************************
. 
.         // relabel for output
.         lab var turnout_urne    "Polling Place Turnout"

.         lab var turnout_pos_req "Mail-in Turnout"

.         lab var turnout_tot_req "Total Turnout"

. 
.         // compute id for DISTANCE increase/decrease, 0 else
.         // ind_dist_up := ids treated precincts that INCREASED dist 
.         gen     tmp = (del_street_dist>0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase in event, 0 else"

.         
.         // ind_dist_dn := ids treated precincts that DECREASED dist 
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease in event, 0 else"

.         
.         // ind_dist_up_high := ids treated precincts that INCREASED dist ABOVE MEDIAN
.         cap drop tmp*

.         sum del_street_dist if K==0 & del_street_dist>0, d      // median = .2624171

            Avg. change in walking distance (km)
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0045026       .0010407
 5%     .0209695       .0045026
10%      .033527       .0048762       Obs                 172
25%     .1229693       .0100222       Sum of wgt.         172

50%     .2566143                      Mean           .3382208
                        Largest       Std. dev.      .2710751
75%     .5199164       .9927621
90%      .753278       .9931583       Variance       .0734817
95%       .86763       .9968569       Skewness       .7802429
99%     .9968569       1.091159       Kurtosis       2.611825

.         gen tmp =  (del_street_dist>=r(p50))                    if K==0
(4,664 missing values generated)

.         
.         bys sb_new (tmp): gen ind_dist_up_high = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up_high = 0                                    if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up_high "=1 if dist increase above median in event, 0 else"

.         
.         // ind_dist_up_low := ids treated precincts that INCREASED dist BELOW MEDIAN
.         gen ind_dist_up_low = ind_dist_up==1 & ind_dist_up_high==0

.         lab var ind_dist_up_low "=1 if dist increase below median in event, 0 else"

.         
.         // ind_dist_dn_high := ids treated precincts that DECREASED dist ABOVE MEDIAN
.         cap drop tmp*

.         sum del_street_dist if K==0 & del_street_dist<0, d 

            Avg. change in walking distance (km)
-------------------------------------------------------------
      Percentiles      Smallest
 1%    -1.056148      -1.361774
 5%    -.6426718      -1.056148
10%    -.5350208      -.7945138       Obs                 108
25%    -.3123866      -.6611561       Sum of wgt.         108

50%    -.1821094                      Mean          -.2330153
                        Largest       Std. dev.      .2229586
75%    -.0775303      -.0100453
90%    -.0240741      -.0077816       Variance       .0497105
95%    -.0113491      -.0056962       Skewness      -2.046262
99%    -.0056962        -.00393       Kurtosis       9.213263

.         gen tmp =  (del_street_dist<= r(p50))                   if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn_high = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn_high = 0                                    if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn_high "=1 if dist decrease above median in event, 0 else"

.         
.         // ind_dist_dn_low := ids treated precincts that DECREASED dist BELOW MEDIAN
.         gen ind_dist_dn_low = ind_dist_dn==1 & ind_dist_dn_high==0

.         lab var ind_dist_dn_low "=1 if dist decrease below median in event, 0 else"

. 
.         
.         // compute group ids: 4 groups ACCORDING to %addresses with dist increase       
.         cap drop tmp*

.         xtile           tmp=street_increased if K==0, n(4)      

.         bys sb_new: egen tmp_group = max(tmp) // ord. var 1-4 for 1/2/3/4th quartile of share ad
> dressses w/ dist increase
(2,704 missing values generated)

.         
.         // gen 4 dummy vars=1 if precints belongs to 1st/2/3/4th quartile, zero else
.         tab tmp_group, gen(ind_shr_up) mis

  tmp_group |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        560       11.33       11.33
          2 |        560       11.33       22.65
          3 |        560       11.33       33.98
          4 |        560       11.33       45.31
          . |      2,704       54.69      100.00
------------+-----------------------------------
      Total |      4,944      100.00

. 
.         bys tmp_group: su del_street_dist street_increased if K==0

--------------------------------------------------------------------------------------------------
-> tmp_group = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
del_street~t |         70   -.3096049    .2347554  -1.361774  -.0337943
street_inc~d |         70    .0998913    .0909934          0   .2763158

--------------------------------------------------------------------------------------------------
-> tmp_group = 2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
del_street~t |         70   -.0109491    .1331585  -.4423189      .5177
street_inc~d |         70    .4747998    .1203837   .2777778    .691358

--------------------------------------------------------------------------------------------------
-> tmp_group = 3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
del_street~t |         70    .2659788    .2009177   .0181849   .9968569
street_inc~d |         70    .8363527    .0788363   .7021276    .956044

--------------------------------------------------------------------------------------------------
-> tmp_group = 4

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
del_street~t |         70    .5261227    .2572387    .033527   1.091159
street_inc~d |         70    .9937309      .01156   .9561403          1

--------------------------------------------------------------------------------------------------
-> tmp_group = .

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
del_street~t |          0
street_inc~d |          0


. 
.         
. ********************************************************************************
.  // Heterogeneity: Increase/Decrease Distance (4 groups), (Figure D8) //
. ********************************************************************************                
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a1 = F`l'event *ind_dist_dn_high                           
>    // a := decrease above median
  3.                 lab var F`l'event_a1 "(N1-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b1 = F`l'event *ind_dist_up_high                   
>            // b := increase above median
  5.                 lab var F`l'event_b1 "(N1+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6.                 gen     F`l'event_a2 = F`l'event *ind_dist_dn_low                            
>    // a := decrease below median
  7.                 lab var F`l'event_a2 "(N2-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  8.                 gen             F`l'event_b2 = F`l'event *ind_dist_up_low                    
>            // b := increase below median
  9.                 lab var F`l'event_b2 "(N2+)x\hspace{.7cm}Reassignment (#t-`l'#)"
 10. 
.                 assert  F`l'event_b1+F`l'event_a1+F`l'event_b2+F`l'event_a2==F`l'event          
 11.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a1 = L`l'event *ind_dist_dn_high      // a := decrease
  3.                 lab var L`l'event_a1 "(N1-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b1 = L`l'event *ind_dist_up_high      // b:= increa
> se
  5.                 lab var L`l'event_b1 "(N1+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 gen     L`l'event_a2 = L`l'event *ind_dist_dn_low       // a := decrease
  7.                 lab var L`l'event_a2 "(N2-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  8.                 gen             L`l'event_b2 = L`l'event *ind_dist_up_low       // b:= increa
> se
  9.                 lab var L`l'event_b2 "(N2+)x\hspace{.7cm}Reassignment (#t+`l'#)"
 10. 
.                 assert  L`l'event_b1+L`l'event_a1+L`l'event_b2+L`l'event_a2==L`l'event
 11.         }               

.         
.         // ORDER dummies
.         order *event_a2, last

.         order *event_b2, last

.         order *event_b1, last

.         order F1event*,last     

.         
.         lab var turnout_urne    "{bf:Panel B.} Effect on Polling Place Turnout"

.         lab var turnout_pos_req "{bf:Panel C.} Effect on Mail-in Turnout"

.         lab var turnout_tot_req "{bf:Panel D.} Effect on Total Turnout"

.         lab var del_street_dist "{bf:Panel A.} Change in Distance"

.         
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         outreg,clear

.         foreach v of varlist del_street_dist turnout_urne turnout_pos_req turnout_tot_req  /*tre
> at_simple*/ {
  2.         
.                 reghdfe `v' F7event_a1-L7event_a1 F7event_a2-L7event_a2 ///
>                                         F7event_b2-L7event_b2 F7event_b1-L7event_b1 ///
>                                         F1event_a1 F1event_a2 F1event_b2 F1event_b1 ///
>                                 $ctr $wgt if smpl_trim ==1, absorb(i.wahl_id#i.stadtbez i.sb_new
> ) cluster(sb_new)
  3.                                 qui outreg,  $opt  keep(F4event_a1-L2event_a1  F4event_b1-L2e
> vent_b1 F4event_a2-L2event_a2  F4event_b2-L2event_b2) store(`v')
  4. 
.                 
.                 estimates store `v'_a1
  5.                 estimates store `v'_a2  
  6.                 estimates store `v'_b2
  7.                 estimates store `v'_b1  
  8. 
.                 
.                 // PLOT Outcome + save
.                 event_plot  `v'_a1 `v'_a2 `v'_b2 `v'_b1 , ///
>                 stub_lag(L#event_a1 L#event_a2 L#event_b2 L#event_b1) stub_lead(F#event_a1 F#eve
> nt_a2 F#event_b2 F#event_b1) plottype(scatter) ciplottype(rcap) ///
>                 together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(small)) xlab
> el(-4(1)2) xtitle("Election since reassignment") ///
>                 legend(pos(12) order(1 "Large distance decrease " 3 "Small distance decrease" 5 
> "Small distance increase" 7 "Large distance increase" ) size(small) col(2) region(style(none))) 
> ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) /// 
>                 title("`:var lab `v''",nobox span bexpand justification(left) size(medium)) ///
>                         name(`v', replace))  ///
>                 lag_opt4(msymbol(S)     msize(2.5pt) color(cranberry))  lag_ci_opt4(color(cranbe
> rry)) ///
>                 lag_opt3(msymbol(Sh)    msize(2.5pt) color(maroon))     lag_ci_opt3(color(maroon
> )) ///
>                 lag_opt2(msymbol(Oh)    msize(2.5pt) color(gray))               lag_ci_opt2(colo
> r(gray)) ///            
>                 lag_opt1(msymbol(O)     msize(2.5pt) color(black))              lag_ci_opt1(colo
> r(black))        
  9. 
. }
(MWFE estimator converged in 8 iterations)
note: L6event_a2 omitted because of collinearity
note: L7event_a2 omitted because of collinearity
note: F1event_a1 omitted because of collinearity
note: F1event_a2 omitted because of collinearity
note: F1event_b2 omitted because of collinearity
note: F1event_b1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  68,    617) =      31.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6349
                                                  Adj R-squared   =     0.5495
                                                  Within R-sq.    =     0.5628
Number of clusters (sb_new)  =        618         Root MSE        =     0.0802

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  del_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_a1 |  -.0469934   .0244987    -1.92   0.056    -.0951043    .0011174
       F6event_a1 |  -.0519591   .0215503    -2.41   0.016      -.09428   -.0096381
       F5event_a1 |  -.0535105     .02073    -2.58   0.010    -.0942203   -.0128007
       F4event_a1 |   -.007875   .0131375    -0.60   0.549    -.0336747    .0179246
       F3event_a1 |   .0027995   .0150386     0.19   0.852    -.0267335    .0323325
       F2event_a1 |   .0109897    .014916     0.74   0.462    -.0183026    .0402819
       L0event_a1 |  -.4156753   .0302854   -13.73   0.000    -.4751501   -.3562004
       L1event_a1 |  -.0065781    .012414    -0.53   0.596    -.0309569    .0178007
       L2event_a1 |   .0055205   .0106767     0.52   0.605    -.0154466    .0264875
       L3event_a1 |   .0435286   .0227893     1.91   0.057    -.0012254    .0882826
       L4event_a1 |  -.0005613   .0234987    -0.02   0.981    -.0467084    .0455858
       L5event_a1 |  -.0195683   .0303809    -0.64   0.520    -.0792308    .0400943
       L6event_a1 |  -.0263426   .0245253    -1.07   0.283    -.0745058    .0218206
       L7event_a1 |  -.0971652   .0449434    -2.16   0.031    -.1854258   -.0089046
       F7event_a2 |   .0107928   .0258629     0.42   0.677    -.0399971    .0615828
       F6event_a2 |   .0111541   .0254114     0.44   0.661    -.0387491    .0610574
       F5event_a2 |   .0479926   .0631004     0.76   0.447     -.075925    .1719101
       F4event_a2 |   .0069178   .0172614     0.40   0.689    -.0269804    .0408161
       F3event_a2 |  -.0005291   .0197327    -0.03   0.979    -.0392806    .0382223
       F2event_a2 |   .0073642   .0178127     0.41   0.679    -.0276165     .042345
       L0event_a2 |  -.0789816   .0227633    -3.47   0.001    -.1236846   -.0342786
       L1event_a2 |   .0235359    .019898     1.18   0.237      -.01554    .0626119
       L2event_a2 |  -.0014795   .0176059    -0.08   0.933    -.0360542    .0330952
       L3event_a2 |  -.0116033   .0226603    -0.51   0.609    -.0561039    .0328973
       L4event_a2 |   .0056373    .016679     0.34   0.735    -.0271171    .0383917
       L5event_a2 |   .0029583   .0321107     0.09   0.927    -.0601012    .0660177
       L6event_a2 |          0  (omitted)
       L7event_a2 |          0  (omitted)
       F7event_b2 |   .0116011   .0115134     1.01   0.314    -.0110092    .0342114
       F6event_b2 |   .0089343   .0103047     0.87   0.386    -.0113022    .0291708
       F5event_b2 |    .002354    .012121     0.19   0.846    -.0214495    .0261575
       F4event_b2 |   -.001589   .0098932    -0.16   0.872    -.0210173    .0178394
       F3event_b2 |  -.0006096   .0103105    -0.06   0.953    -.0208576    .0196384
       F2event_b2 |   .0061477   .0091759     0.67   0.503    -.0118721    .0241674
       L0event_b2 |   .1170968    .013135     8.91   0.000     .0913021    .1428916
       L1event_b2 |   .0037918   .0105161     0.36   0.719    -.0168599    .0244435
       L2event_b2 |    .002859   .0338126     0.08   0.933    -.0635428    .0692608
       L3event_b2 |  -.0557115   .0253895    -2.19   0.029    -.1055717   -.0058512
       L4event_b2 |   .0093993   .0214606     0.44   0.662    -.0327453    .0515439
       L5event_b2 |  -.0432591   .0388632    -1.11   0.266    -.1195793    .0330612
       L6event_b2 |  -.0042357   .0207978    -0.20   0.839    -.0450788    .0366074
       L7event_b2 |  -.0455408   .0434041    -1.05   0.294    -.1307784    .0396968
       F7event_b1 |   .0011025   .0171965     0.06   0.949    -.0326683    .0348733
       F6event_b1 |   -.005066   .0129731    -0.39   0.696    -.0305427    .0204107
       F5event_b1 |   .0013005   .0115129     0.11   0.910    -.0213087    .0239097
       F4event_b1 |  -.0023747   .0072825    -0.33   0.744    -.0166763    .0119268
       F3event_b1 |  -.0125612   .0103149    -1.22   0.224    -.0328178    .0076955
       F2event_b1 |    .005808   .0079599     0.73   0.466    -.0098237    .0214397
       L0event_b1 |   .5400293   .0237543    22.73   0.000     .4933802    .5866784
       L1event_b1 |   -.027127   .0207024    -1.31   0.191    -.0677828    .0135287
       L2event_b1 |  -.0290466   .0191075    -1.52   0.129    -.0665703     .008477
       L3event_b1 |  -.2108101    .057305    -3.68   0.000    -.3233465   -.0982737
       L4event_b1 |  -.1431316   .0306696    -4.67   0.000    -.2033612   -.0829021
       L5event_b1 |    -.08386   .0242294    -3.46   0.001    -.1314421   -.0362779
       L6event_b1 |  -.0880442   .0243334    -3.62   0.000    -.1358304   -.0402579
       L7event_b1 |  -.0815251   .0418377    -1.95   0.052    -.1636867    .0006365
       F1event_a1 |          0  (omitted)
       F1event_a2 |          0  (omitted)
       F1event_b2 |          0  (omitted)
       F1event_b1 |          0  (omitted)
        ln_ew_ges |  -.0971146   .0486859    -1.99   0.047    -.1927248   -.0015043
         ew_biodt |  -.0026599   .0014953    -1.78   0.076    -.0055963    .0002766
        ew_dtmihi |  -.0069317   .0023495    -2.95   0.003    -.0115457   -.0023176
         ew_ledig |  -.0007266   .0033046    -0.22   0.826    -.0072161     .005763
       ew_married |  -.0032964    .003142    -1.05   0.295    -.0094667    .0028739
        wb_anteil |   .0010637    .001325     0.80   0.422    -.0015383    .0036657
          wb_ausl |  -.0006526   .0005348    -1.22   0.223    -.0017028    .0003977
         wb_18t24 |    .000028   .0010793     0.03   0.979    -.0020916    .0021477
         wb_25t34 |   .0004862   .0006804     0.71   0.475    -.0008501    .0018224
         wb_35t44 |  -.0007289   .0009335    -0.78   0.435    -.0025622    .0011044
         wb_45t59 |   .0009494   .0008156     1.16   0.245    -.0006523     .002551
          avg_dur |   -.000261   .0008612    -0.30   0.762    -.0019523    .0014304
          hh_kids |   .0046954   .0017966     2.61   0.009     .0011673    .0082235
mpreis_flats_rent |   -.000139   .0012108    -0.11   0.909    -.0025169    .0022388
            _cons |   1.020961    .422106     2.42   0.016     .1920227      1.8499
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_a2 omitted because of collinearity
note: L7event_a2 omitted because of collinearity
note: F1event_a1 omitted because of collinearity
note: F1event_a2 omitted because of collinearity
note: F1event_b2 omitted because of collinearity
note: F1event_b1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  68,    617) =      15.42
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9741
                                                  Adj R-squared   =     0.9681
                                                  Within R-sq.    =     0.2329
Number of clusters (sb_new)  =        618         Root MSE        =     1.6400

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_a1 |   1.710171   .7552051     2.26   0.024     .2270865    3.193255
       F6event_a1 |   1.800765   .8282989     2.17   0.030     .1741377    3.427392
       F5event_a1 |   1.591113   .6308725     2.52   0.012     .3521948     2.83003
       F4event_a1 |   .3239985   .3429601     0.94   0.345    -.3495122    .9975092
       F3event_a1 |   .2468047   .3418335     0.72   0.471    -.4244935    .9181028
       F2event_a1 |   .0096129   .2154032     0.04   0.964    -.4133993    .4326252
       L0event_a1 |   1.626675   .5016922     3.24   0.001     .6414436    2.611906
       L1event_a1 |   1.452389   .4053213     3.58   0.000     .6564122    2.248365
       L2event_a1 |   1.412316    .426705     3.31   0.001     .5743459    2.250286
       L3event_a1 |   1.641113   .4305719     3.81   0.000     .7955487    2.486677
       L4event_a1 |   1.687831   .7920739     2.13   0.033     .1323437    3.243319
       L5event_a1 |   1.705906   .9523515     1.79   0.074    -.1643375    3.576149
       L6event_a1 |   4.066919   1.139174     3.57   0.000     1.829789    6.304048
       L7event_a1 |   1.447566   .6040565     2.40   0.017     .2613099    2.633822
       F7event_a2 |  -.5114505   .6677004    -0.77   0.444    -1.822691    .7997905
       F6event_a2 |  -1.018243   .5799577    -1.76   0.080    -2.157174    .1206869
       F5event_a2 |  -.5771003   .3934982    -1.47   0.143    -1.349858    .1956579
       F4event_a2 |    -.55343   .2863425    -1.93   0.054    -1.115754    .0088941
       F3event_a2 |   -.696219   .2910418    -2.39   0.017    -1.267772   -.1246664
       F2event_a2 |  -.3726712   .2493008    -1.49   0.135    -.8622522    .1169097
       L0event_a2 |  -.5200322   .4063232    -1.28   0.201    -1.317976    .2779119
       L1event_a2 |  -.2227994   .4533491    -0.49   0.623    -1.113094    .6674949
       L2event_a2 |   -.591167   .4501163    -1.31   0.190    -1.475113    .2927788
       L3event_a2 |  -.3390838   .4670946    -0.73   0.468    -1.256372    .5782042
       L4event_a2 |  -1.141944   .7973462    -1.43   0.153    -2.707785    .4238978
       L5event_a2 |  -.2012999   1.488929    -0.14   0.892    -3.125283    2.722683
       L6event_a2 |          0  (omitted)
       L7event_a2 |          0  (omitted)
       F7event_b2 |  -1.002429   .5730869    -1.75   0.081    -2.127866    .1230088
       F6event_b2 |  -.2558241   .4765268    -0.54   0.592    -1.191635     .679987
       F5event_b2 |   .4864205    .367925     1.32   0.187    -.2361166    1.208958
       F4event_b2 |   .3477822   .2770897     1.26   0.210     -.196371    .8919354
       F3event_b2 |  -.0142352   .2402368    -0.06   0.953    -.4860161    .4575457
       F2event_b2 |   .1973618   .1867706     1.06   0.291    -.1694214     .564145
       L0event_b2 |   -.837223   .2985052    -2.80   0.005    -1.423432   -.2510136
       L1event_b2 |  -1.239775    .302142    -4.10   0.000    -1.833126   -.6464236
       L2event_b2 |  -.8615286   .3717338    -2.32   0.021    -1.591545   -.1315117
       L3event_b2 |  -.5684927    .375704    -1.51   0.131    -1.306306     .169321
       L4event_b2 |  -.9582043   .6730322    -1.42   0.155    -2.279916    .3635072
       L5event_b2 |  -.7787905   .7989854    -0.97   0.330    -2.347851    .7902701
       L6event_b2 |    .631508   .5175049     1.22   0.223    -.3847764    1.647792
       L7event_b2 |   4.288513   .5542234     7.74   0.000      3.20012    5.376906
       F7event_b1 |  -.1560496   .4892945    -0.32   0.750    -1.116934    .8048348
       F6event_b1 |    .161494   .4549106     0.36   0.723    -.7318667    1.054855
       F5event_b1 |   -.070132   .4205705    -0.17   0.868    -.8960552    .7557913
       F4event_b1 |  -.1498475   .2826496    -0.53   0.596    -.7049193    .4052243
       F3event_b1 |    .093904   .3048409     0.31   0.758    -.5047475    .6925555
       F2event_b1 |   .0776878   .2142389     0.36   0.717    -.3430381    .4984138
       L0event_b1 |  -2.896049   .3821345    -7.58   0.000    -3.646491   -2.145607
       L1event_b1 |  -2.682076    .414531    -6.47   0.000    -3.496139   -1.868013
       L2event_b1 |  -2.301017   .4547327    -5.06   0.000    -3.194029   -1.408006
       L3event_b1 |  -1.553241   .5327192    -2.92   0.004    -2.599404   -.5070787
       L4event_b1 |  -1.928667   1.060879    -1.82   0.070    -4.012038     .154703
       L5event_b1 |    -1.1313    1.14532    -0.99   0.324    -3.380498    1.117898
       L6event_b1 |  -1.361197   .8423013    -1.62   0.107    -3.015322    .2929275
       L7event_b1 |  -.6929844   .6653005    -1.04   0.298    -1.999512    .6135436
       F1event_a1 |          0  (omitted)
       F1event_a2 |          0  (omitted)
       F1event_b2 |          0  (omitted)
       F1event_b1 |          0  (omitted)
        ln_ew_ges |  -1.046894    .927609    -1.13   0.260    -2.868548    .7747596
         ew_biodt |   .3716659   .0275553    13.49   0.000     .3175523    .4257795
        ew_dtmihi |   .0709154    .049883     1.42   0.156    -.0270456    .1688764
         ew_ledig |   .2253927   .0533791     4.22   0.000      .120566    .3302193
       ew_married |   .4049879   .0544002     7.44   0.000      .298156    .5118198
        wb_anteil |  -.2863893   .0199061   -14.39   0.000    -.3254811   -.2472975
          wb_ausl |   .0215794   .0155056     1.39   0.165    -.0088707    .0520295
         wb_18t24 |  -.0167537   .0303083    -0.55   0.581    -.0762736    .0427662
         wb_25t34 |  -.0639594   .0187631    -3.41   0.001    -.1008067   -.0271121
         wb_35t44 |   .0059994   .0219915     0.27   0.785    -.0371878    .0491866
         wb_45t59 |    .014599   .0214872     0.68   0.497     -.027598     .056796
          avg_dur |  -.0223076   .0201026    -1.11   0.268    -.0617853    .0171701
          hh_kids |  -.0478645   .0387433    -1.24   0.217    -.1239491    .0282202
mpreis_flats_rent |   .0334861   .0243318     1.38   0.169     -.014297    .0812693
            _cons |   13.02417   8.533191     1.53   0.127    -3.733452    29.78179
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_a2 omitted because of collinearity
note: L7event_a2 omitted because of collinearity
note: F1event_a1 omitted because of collinearity
note: F1event_a2 omitted because of collinearity
note: F1event_b2 omitted because of collinearity
note: F1event_b1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  68,    617) =      14.22
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9636
                                                  Adj R-squared   =     0.9551
                                                  Within R-sq.    =     0.2458
Number of clusters (sb_new)  =        618         Root MSE        =     1.6485

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_a1 |  -1.102684   .4536483    -2.43   0.015    -1.993566   -.2118022
       F6event_a1 |  -.6763817   .4521135    -1.50   0.135     -1.56425    .2114862
       F5event_a1 |  -1.706532   .5515195    -3.09   0.002    -2.789615   -.6234487
       F4event_a1 |  -.4082871   .2796331    -1.46   0.145    -.9574351     .140861
       F3event_a1 |  -.0578426   .2772667    -0.21   0.835    -.6023435    .4866583
       F2event_a1 |  -.1097564   .2354219    -0.47   0.641    -.5720817    .3525689
       L0event_a1 |  -1.202927   .4586034    -2.62   0.009     -2.10354   -.3023143
       L1event_a1 |  -1.045953   .4116018    -2.54   0.011    -1.854263   -.2376426
       L2event_a1 |  -.7482738   .4145634    -1.80   0.072      -1.5624    .0658525
       L3event_a1 |   -1.53277   .3660467    -4.19   0.000    -2.251619    -.813922
       L4event_a1 |  -2.088095   1.219368    -1.71   0.087    -4.482709    .3065189
       L5event_a1 |   -.881246   .6991396    -1.26   0.208    -2.254228    .4917358
       L6event_a1 |  -4.088938   .7801945    -5.24   0.000    -5.621096   -2.556779
       L7event_a1 |  -2.939201   .6291799    -4.67   0.000    -4.174794   -1.703607
       F7event_a2 |  -.5791567    .743303    -0.78   0.436    -2.038867    .8805537
       F6event_a2 |   .1275559    .620272     0.21   0.837    -1.090544    1.345656
       F5event_a2 |  -.6958843    .534795    -1.30   0.194    -1.746123    .3543549
       F4event_a2 |  -.1965683   .3034272    -0.65   0.517    -.7924436     .399307
       F3event_a2 |   .3127098   .3124379     1.00   0.317    -.3008608    .9262804
       F2event_a2 |   .0094543   .2460798     0.04   0.969    -.4738012    .4927099
       L0event_a2 |   .1574637   .3571371     0.44   0.659     -.543888    .8588155
       L1event_a2 |    .280778   .4157741     0.68   0.500    -.5357259    1.097282
       L2event_a2 |   1.137092   .5030692     2.26   0.024     .1491562    2.125027
       L3event_a2 |   .6282571   .4186177     1.50   0.134    -.1938312    1.450345
       L4event_a2 |   2.402119    .829758     2.89   0.004     .7726269    4.031611
       L5event_a2 |   2.856078   1.078404     2.65   0.008     .7382903    4.973865
       L6event_a2 |          0  (omitted)
       L7event_a2 |          0  (omitted)
       F7event_b2 |     .42283   .5035726     0.84   0.401    -.5660941    1.411754
       F6event_b2 |   .7120051   .3232704     2.20   0.028     .0771613    1.346849
       F5event_b2 |  -.7461013   .3812987    -1.96   0.051    -1.494902    .0026992
       F4event_b2 |  -.3952969   .2631429    -1.50   0.134    -.9120612    .1214674
       F3event_b2 |   .0771937   .2637803     0.29   0.770    -.4408223    .5952097
       F2event_b2 |  -.1700671   .1869431    -0.91   0.363    -.5371891    .1970548
       L0event_b2 |   .5209591   .3245064     1.61   0.109    -.1163117     1.15823
       L1event_b2 |   1.039885   .3404111     3.05   0.002     .3713806     1.70839
       L2event_b2 |    .686095   .4039461     1.70   0.090    -.1071808    1.479371
       L3event_b2 |   .6473044   .4704191     1.38   0.169    -.2765123    1.571121
       L4event_b2 |   2.681524   .6443581     4.16   0.000     1.416123    3.946925
       L5event_b2 |   2.170884    .662887     3.27   0.001     .8690958    3.472673
       L6event_b2 |   .2248853   .5561716     0.40   0.686    -.8673335    1.317104
       L7event_b2 |  -1.811712   .6176439    -2.93   0.003    -3.024652   -.5987734
       F7event_b1 |   .7659576   .3742958     2.05   0.041     .0309094    1.501006
       F6event_b1 |    .321991   .3301049     0.98   0.330    -.3262744    .9702564
       F5event_b1 |   .3552163   .4230183     0.84   0.401    -.4755138    1.185946
       F4event_b1 |   .0106634    .244594     0.04   0.965    -.4696743    .4910011
       F3event_b1 |  -.1869845   .2239527    -0.83   0.404    -.6267865    .2528175
       F2event_b1 |   .0184691   .2086987     0.09   0.930    -.3913767     .428315
       L0event_b1 |   1.955754   .3480428     5.62   0.000     1.272262    2.639246
       L1event_b1 |   2.635784   .3588773     7.34   0.000     1.931015    3.340553
       L2event_b1 |   2.785198   .4433834     6.28   0.000     1.914475    3.655922
       L3event_b1 |   1.825292   .4272718     4.27   0.000      .986209    2.664376
       L4event_b1 |   2.794659   .6546916     4.27   0.000     1.508965    4.080353
       L5event_b1 |   3.856796    .979995     3.94   0.000     1.932266    5.781326
       L6event_b1 |   3.149503   .6541049     4.81   0.000     1.864962    4.434045
       L7event_b1 |   2.903492   .6041692     4.81   0.000     1.717015     4.08997
       F1event_a1 |          0  (omitted)
       F1event_a2 |          0  (omitted)
       F1event_b2 |          0  (omitted)
       F1event_b1 |          0  (omitted)
        ln_ew_ges |   2.669047   1.344608     1.98   0.048     .0284831     5.30961
         ew_biodt |    .392074   .0281761    13.92   0.000     .3367413    .4474067
        ew_dtmihi |  -.2392872   .0589037    -4.06   0.000    -.3549633   -.1236112
         ew_ledig |   .1959985   .0737537     2.66   0.008     .0511599    .3408372
       ew_married |   .2265502   .0740757     3.06   0.002     .0810792    .3720212
        wb_anteil |   -.247526   .0217198   -11.40   0.000    -.2901796   -.2048724
          wb_ausl |  -.0717966   .0142967    -5.02   0.000    -.0998726   -.0437206
         wb_18t24 |  -.0323693   .0276772    -1.17   0.243    -.0867223    .0219837
         wb_25t34 |    .045543   .0189983     2.40   0.017     .0082339    .0828521
         wb_35t44 |  -.0053087   .0236419    -0.22   0.822    -.0517371    .0411198
         wb_45t59 |  -.0376526   .0202083    -1.86   0.063    -.0773379    .0020328
          avg_dur |   .0436461   .0222376     1.96   0.050    -.0000245    .0873167
          hh_kids |  -.0665779   .0407918    -1.63   0.103    -.1466855    .0135297
mpreis_flats_rent |  -.0210719   .0231139    -0.91   0.362    -.0664634    .0243197
            _cons |  -12.70761   11.12948    -1.14   0.254    -34.56386    9.148647
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_a2 omitted because of collinearity
note: L7event_a2 omitted because of collinearity
note: F1event_a1 omitted because of collinearity
note: F1event_a2 omitted because of collinearity
note: F1event_b2 omitted because of collinearity
note: F1event_b1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  68,    617) =      23.54
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9905
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4480
Number of clusters (sb_new)  =        618         Root MSE        =     1.6207

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_a1 |   .6074855   1.012982     0.60   0.549    -1.381826    2.596797
       F6event_a1 |   1.124381   .6746059     1.67   0.096    -.2004215    2.449183
       F5event_a1 |  -.1154192   .8585317    -0.13   0.893    -1.801418    1.570579
       F4event_a1 |  -.0842887   .3491267    -0.24   0.809    -.7699093     .601332
       F3event_a1 |   .1889614   .3226964     0.59   0.558     -.444755    .8226778
       F2event_a1 |  -.1001431   .3080357    -0.33   0.745    -.7050686    .5047825
       L0event_a1 |   .4237466   .3191311     1.33   0.185    -.2029683    1.050461
       L1event_a1 |   .4064359   .3868152     1.05   0.294    -.3531981     1.16607
       L2event_a1 |   .6640423   .3977751     1.67   0.096    -.1171149    1.445199
       L3event_a1 |   .1083421   .3521653     0.31   0.758    -.5832459    .7999301
       L4event_a1 |  -.4002634   1.298746    -0.31   0.758    -2.950763    2.150236
       L5event_a1 |   .8246587   1.290148     0.64   0.523    -1.708955    3.358273
       L6event_a1 |  -.0220198   1.476551    -0.01   0.988    -2.921695    2.877655
       L7event_a1 |  -1.491633   .7291959    -2.05   0.041     -2.92364   -.0596266
       F7event_a2 |  -1.090609   .4332415    -2.52   0.012    -1.941416   -.2398022
       F6event_a2 |  -.8906884   .3594089    -2.48   0.013    -1.596501   -.1848754
       F5event_a2 |  -1.272984   .4909933    -2.59   0.010    -2.237205   -.3087634
       F4event_a2 |  -.7499987   .2929041    -2.56   0.011    -1.325209   -.1747887
       F3event_a2 |  -.3835098    .259915    -1.48   0.141    -.8939351    .1269154
       F2event_a2 |   -.363217   .2448784    -1.48   0.139    -.8441132    .1176792
       L0event_a2 |  -.3625684   .3221792    -1.13   0.261    -.9952692    .2701324
       L1event_a2 |   .0579783   .4041751     0.14   0.886    -.7357474     .851704
       L2event_a2 |   .5459256    .417528     1.31   0.192    -.2740226    1.365874
       L3event_a2 |   .2891734   .3914453     0.74   0.460    -.4795531      1.0579
       L4event_a2 |   1.260177   .6377679     1.98   0.049     .0077176    2.512636
       L5event_a2 |   2.654779   1.755226     1.51   0.131    -.7921618    6.101721
       L6event_a2 |          0  (omitted)
       L7event_a2 |          0  (omitted)
       F7event_b2 |  -.5795989   .5825614    -0.99   0.320    -1.723642    .5644445
       F6event_b2 |   .4561803   .5354863     0.85   0.395    -.5954164    1.507777
       F5event_b2 |  -.2596803   .4286243    -0.61   0.545     -1.10142     .582059
       F4event_b2 |   -.047514   .2590304    -0.18   0.855    -.5562022    .4611742
       F3event_b2 |   .0629583   .2560682     0.25   0.806    -.4399125    .5658291
       F2event_b2 |   .0272952   .2238209     0.12   0.903    -.4122478    .4668383
       L0event_b2 |  -.3162631   .2631835    -1.20   0.230    -.8331071    .2005808
       L1event_b2 |  -.1998888   .3492411    -0.57   0.567     -.885734    .4859565
       L2event_b2 |  -.1754345   .3329644    -0.53   0.598    -.8293153    .4784464
       L3event_b2 |   .0788124   .3851002     0.20   0.838    -.6774536    .8350783
       L4event_b2 |   1.723319   .6132404     2.81   0.005     .5190275    2.927611
       L5event_b2 |   1.392094   .4897551     2.84   0.005     .4303052    2.353883
       L6event_b2 |   .8563892   .5631963     1.52   0.129    -.2496247    1.962403
       L7event_b2 |   2.476802   .5078185     4.88   0.000      1.47954    3.474064
       F7event_b1 |   .6099092   .3465262     1.76   0.079    -.0706046    1.290423
       F6event_b1 |   .4834848   .3810256     1.27   0.205    -.2647794    1.231749
       F5event_b1 |   .2850847   .3167148     0.90   0.368     -.336885    .9070544
       F4event_b1 |  -.1391835   .2720561    -0.51   0.609    -.6734516    .3950846
       F3event_b1 |  -.0930803    .266331    -0.35   0.727    -.6161055    .4299449
       F2event_b1 |   .0961577   .2159678     0.45   0.656    -.3279634    .5202788
       L0event_b1 |  -.9402939   .2867685    -3.28   0.001    -1.503455   -.3771331
       L1event_b1 |  -.0462918   .3425063    -0.14   0.893    -.7189112    .6263275
       L2event_b1 |   .4841819   .4240305     1.14   0.254     -.348536      1.3169
       L3event_b1 |   .2720516    .450466     0.60   0.546    -.6125809    1.156684
       L4event_b1 |    .865992   1.144041     0.76   0.449    -1.380694    3.112678
       L5event_b1 |   2.725493   1.822545     1.50   0.135    -.8536506    6.304636
       L6event_b1 |   1.788304    .815457     2.19   0.029      .186896    3.389712
       L7event_b1 |   2.210507   .8009545     2.76   0.006     .6375792    3.783434
       F1event_a1 |          0  (omitted)
       F1event_a2 |          0  (omitted)
       F1event_b2 |          0  (omitted)
       F1event_b1 |          0  (omitted)
        ln_ew_ges |   1.622153   1.067552     1.52   0.129    -.4743225    3.718628
         ew_biodt |   .7637399   .0314938    24.25   0.000     .7018918     .825588
        ew_dtmihi |  -.1683717   .0520823    -3.23   0.001    -.2706518   -.0660916
         ew_ledig |   .4213914   .0720619     5.85   0.000      .279875    .5629077
       ew_married |   .6315382   .0701574     9.00   0.000     .4937619    .7693145
        wb_anteil |  -.5339153    .024068   -22.18   0.000    -.5811805   -.4866502
          wb_ausl |  -.0502172   .0174899    -2.87   0.004    -.0845643   -.0158702
         wb_18t24 |   -.049123   .0260271    -1.89   0.060    -.1002355    .0019896
         wb_25t34 |  -.0184164   .0167032    -1.10   0.271    -.0512184    .0143855
         wb_35t44 |   .0006908   .0209173     0.03   0.974     -.040387    .0417686
         wb_45t59 |  -.0230535   .0196019    -1.18   0.240    -.0615481     .015441
          avg_dur |   .0213385   .0221352     0.96   0.335     -.022131    .0648079
          hh_kids |  -.1144425   .0357698    -3.20   0.001    -.1846878   -.0441973
mpreis_flats_rent |   .0124143   .0234961     0.53   0.597    -.0337277    .0585562
            _cons |   .3165471   9.970608     0.03   0.975    -19.26389    19.89699
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
.         * PLOT: FIGURE D8. Effect Heterogeneity by Change in Proximity to the Polling Location
.         grc1leg del_street_dist turnout_urne turnout_pos_req turnout_tot_req, col(2)  xcommon po
> s(6) imargins(small)

.         gr_edit .plotregion1.graph1.yaxis1.reset_rule -0.6 0.6 .3 , tickset(major) ruletype(rang
> e) 

.         gr_edit .plotregion1.graph1.yaxis1.title.text = {}

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"Change in distance in km"'

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"(estimates)"'

.         gr_edit .style.editstyle declared_ysize(4) editcopy

. 
.         graph export "$figures/Figure_D8_ES_het_by_distance_4clust.pdf", replace        
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D8_ES_
    > het_by_distance_4clust.pdf saved as PDF format

. 
. 
. ********************************************************************************
.  // Heterogeneity: 4 groups by SHARE of addresses with distance up in K=0 (Figure D10)
. ********************************************************************************                
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a1 = F`l'event *ind_shr_up1                           // a 
> := decrease above median
  3.                 lab var F`l'event_a1 "(N1-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b1 = F`l'event *ind_shr_up4                        
>    // b:= increase
  5.                 lab var F`l'event_b1 "(N1+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6.                 gen     F`l'event_a2 = F`l'event *ind_shr_up2                           // a 
> := decrease below median
  7.                 lab var F`l'event_a2 "(N2-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  8.                 gen             F`l'event_b2 = F`l'event *ind_shr_up3                        
>    // b:= increase
  9.                 lab var F`l'event_b2 "(N2+)x\hspace{.7cm}Reassignment (#t-`l'#)"
 10. 
.                 assert  F`l'event_b1+F`l'event_a1+F`l'event_b2+F`l'event_a2==F`l'event          
 11.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a1 = L`l'event *ind_shr_up1   // a := decrease
  3.                 lab var L`l'event_a1 "(N1-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b1 = L`l'event *ind_shr_up4   // b:= increase
  5.                 lab var L`l'event_b1 "(N1+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 gen     L`l'event_a2 = L`l'event *ind_shr_up2   // a := decrease
  7.                 lab var L`l'event_a2 "(N2-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  8.                 gen             L`l'event_b2 = L`l'event *ind_shr_up3   // b:= increase
  9.                 lab var L`l'event_b2 "(N2+)x\hspace{.7cm}Reassignment (#t+`l'#)"
 10. 
.                 assert  L`l'event_b1+L`l'event_a1+L`l'event_b2+L`l'event_a2==L`l'event
 11.         }               

.         
.         // ORDER dummies
.         order *event_a2, last

.         order *event_b2, last

.         order *event_b1, last

.         order F1event*,last     

.         
.         lab var turnout_urne    "{bf:Panel B.} Effect on Polling Place Turnout"

.         lab var turnout_pos_req "{bf:Panel C.} Effect on Mail-in Turnout"

.         lab var turnout_tot_req "{bf:Panel D.} Effect on Total Turnout"

.         lab var del_street_dist "{bf:Panel A.} Change in Distance"

.         
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         outreg,clear

.         foreach v of varlist del_street_dist turnout_urne turnout_pos_req turnout_tot_req  /*tre
> at_simple*/ {
  2.         
.                 reghdfe `v' F7event_a1-L7event_a1 F7event_a2-L7event_a2 ///
>                                         F7event_b2-L7event_b2 F7event_b1-L7event_b1 ///
>                                         F1event_a1 F1event_a2 F1event_b2 F1event_b1 ///
>                                 $ctr $wgt if smpl_trim ==1, absorb(i.wahl_id#i.stadtbez i.sb_new
> ) cluster(sb_new)
  3.                                 qui outreg,  $opt  keep(F4event_a1-L2event_a1  F4event_b1-L2e
> vent_b1 F4event_a2-L2event_a2  F4event_b2-L2event_b2) store(`v')
  4. 
.                 
.                 estimates store `v'_a1
  5.                 estimates store `v'_a2  
  6.                 estimates store `v'_b2
  7.                 estimates store `v'_b1  
  8. 
.                 
.                 // PLOT Outcome + save
.                 event_plot  `v'_a1 `v'_a2 `v'_b2 `v'_b1 , ///
>                 stub_lag(L#event_a1 L#event_a2 L#event_b2 L#event_b1) stub_lead(F#event_a1 F#eve
> nt_a2 F#event_b2 F#event_b1) plottype(scatter) ciplottype(rcap) ///
>                 together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(small)) xlab
> el(-4(1)2) xtitle("Election since reassignment") ///
>                 legend(pos(12) order(1 "0-27%" 3 "29-69%" 5 "71-95%" 7 "96-100%" ) size(small) c
> ol(2) region(style(none)) title("% Addresses with distance increase in t=0")) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) /// 
>                 title("`:var lab `v''",nobox span bexpand justification(left) size(medium)) ///
>                         name(`v', replace))  ///
>                 lag_opt4(msymbol(S)     msize(2.5pt) color(cranberry))  lag_ci_opt4(color(cranbe
> rry)) ///
>                 lag_opt3(msymbol(Sh)    msize(2.5pt) color(maroon))     lag_ci_opt3(color(maroon
> )) ///
>                 lag_opt2(msymbol(Oh)    msize(2.5pt) color(gray))               lag_ci_opt2(colo
> r(gray)) ///            
>                 lag_opt1(msymbol(O)     msize(2.5pt) color(black))              lag_ci_opt1(colo
> r(black))        
  9. 
. }
(MWFE estimator converged in 8 iterations)
note: L6event_a2 omitted because of collinearity
note: L7event_a2 omitted because of collinearity
note: F1event_a1 omitted because of collinearity
note: F1event_a2 omitted because of collinearity
note: F1event_b2 omitted because of collinearity
note: F1event_b1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  68,    617) =      16.37
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5625
                                                  Adj R-squared   =     0.4602
                                                  Within R-sq.    =     0.4761
Number of clusters (sb_new)  =        618         Root MSE        =     0.0878

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  del_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_a1 |  -.0460068   .0188778    -2.44   0.015    -.0830793   -.0089343
       F6event_a1 |  -.0420318   .0154234    -2.73   0.007    -.0723205    -.011743
       F5event_a1 |  -.0414585   .0155236    -2.67   0.008     -.071944   -.0109729
       F4event_a1 |  -.0077578   .0109174    -0.71   0.478    -.0291976     .013682
       F3event_a1 |  -.0223707   .0105885    -2.11   0.035    -.0431646   -.0015769
       F2event_a1 |   .0045313   .0119012     0.38   0.704    -.0188404    .0279031
       L0event_a1 |  -.3201551    .029029   -11.03   0.000    -.3771627   -.2631475
       L1event_a1 |   .0040634   .0092656     0.44   0.661    -.0141324    .0222593
       L2event_a1 |   .0037994   .0091457     0.42   0.678    -.0141611    .0217599
       L3event_a1 |   .0273102   .0195587     1.40   0.163    -.0110995    .0657199
       L4event_a1 |   .0128567   .0207389     0.62   0.536    -.0278708    .0535842
       L5event_a1 |  -.0236756   .0252408    -0.94   0.349    -.0732438    .0258927
       L6event_a1 |  -.0229671    .022589    -1.02   0.310    -.0673277    .0213936
       L7event_a1 |  -.0820965   .0503438    -1.63   0.103    -.1809625    .0167694
       F7event_a2 |  -.0019465   .0189073    -0.10   0.918    -.0390769    .0351839
       F6event_a2 |  -.0014571   .0210337    -0.07   0.945    -.0427635    .0398492
       F5event_a2 |   .0249377   .0474582     0.53   0.599    -.0682615    .1181369
       F4event_a2 |   .0013025   .0164894     0.08   0.937    -.0310796    .0336845
       F3event_a2 |   .0134728    .019553     0.69   0.491    -.0249256    .0518712
       F2event_a2 |   .0166174   .0171077     0.97   0.332    -.0169791    .0502138
       L0event_a2 |  -.0131278   .0259503    -0.51   0.613    -.0640894    .0378337
       L1event_a2 |   .0115639   .0200889     0.58   0.565     -.027887    .0510147
       L2event_a2 |   .0117539   .0518148     0.23   0.821     -.090001    .1135087
       L3event_a2 |   -.069329   .0313574    -2.21   0.027    -.1309091   -.0077489
       L4event_a2 |  -.0227554   .0359849    -0.63   0.527    -.0934231    .0479123
       L5event_a2 |  -.0292707   .0324369    -0.90   0.367    -.0929707    .0344293
       L6event_a2 |          0  (omitted)
       L7event_a2 |          0  (omitted)
       F7event_b2 |  -.0032422   .0176396    -0.18   0.854    -.0378832    .0313988
       F6event_b2 |  -.0104007   .0133013    -0.78   0.435     -.036522    .0157205
       F5event_b2 |  -.0080468    .010708    -0.75   0.453    -.0290754    .0129818
       F4event_b2 |  -.0109731   .0064195    -1.71   0.088    -.0235799    .0016336
       F3event_b2 |  -.0105836   .0068277    -1.55   0.122    -.0239919    .0028247
       F2event_b2 |  -.0010935   .0054073    -0.20   0.840    -.0117123    .0095254
       L0event_b2 |   .2618388   .0238463    10.98   0.000     .2150091    .3086686
       L1event_b2 |  -.0186709   .0231525    -0.81   0.420    -.0641382    .0267964
       L2event_b2 |  -.0055067   .0101911    -0.54   0.589    -.0255202    .0145067
       L3event_b2 |  -.1305753   .0463017    -2.82   0.005    -.2215034   -.0396472
       L4event_b2 |   .0645492   .0406736     1.59   0.113    -.0153262    .1444247
       L5event_b2 |  -.0112132   .0407381    -0.28   0.783    -.0912154     .068789
       L6event_b2 |    .007144   .0218547     0.33   0.744    -.0357747    .0500626
       L7event_b2 |  -.0405056    .049724    -0.81   0.416    -.1381544    .0571432
       F7event_b1 |  -.0239266   .0211248    -1.13   0.258    -.0654119    .0175586
       F6event_b1 |  -.0193149   .0164478    -1.17   0.241    -.0516154    .0129856
       F5event_b1 |   -.000226   .0147238    -0.02   0.988    -.0291407    .0286888
       F4event_b1 |  -.0025697   .0101775    -0.25   0.801    -.0225564     .017417
       F3event_b1 |  -.0111407   .0135141    -0.82   0.410    -.0376798    .0153985
       F2event_b1 |   .0075076   .0102835     0.73   0.466    -.0126873    .0277025
       L0event_b1 |   .5233182    .031226    16.76   0.000      .461996    .5846403
       L1event_b1 |   .0060805   .0115533     0.53   0.599    -.0166082    .0287691
       L2event_b1 |  -.0188581    .022294    -0.85   0.398    -.0626393    .0249232
       L3event_b1 |  -.1484513   .0586541    -2.53   0.012    -.2636372   -.0332655
       L4event_b1 |  -.0211681   .0459374    -0.46   0.645    -.1113807    .0690444
       L5event_b1 |  -.0316604   .0361022    -0.88   0.381    -.1025585    .0392377
       L6event_b1 |  -.0432077   .0258409    -1.67   0.095    -.0939545    .0075391
       L7event_b1 |  -.0543748    .042524    -1.28   0.201    -.1378841    .0291345
       F1event_a1 |          0  (omitted)
       F1event_a2 |          0  (omitted)
       F1event_b2 |          0  (omitted)
       F1event_b1 |          0  (omitted)
        ln_ew_ges |  -.1110523   .0552659    -2.01   0.045    -.2195844   -.0025202
         ew_biodt |  -.0028719   .0015647    -1.84   0.067    -.0059448     .000201
        ew_dtmihi |  -.0055073   .0027506    -2.00   0.046    -.0109091   -.0001056
         ew_ledig |   -.002615   .0048931    -0.53   0.593    -.0122242    .0069942
       ew_married |  -.0048318   .0045415    -1.06   0.288    -.0137505    .0040868
        wb_anteil |   .0010752   .0014101     0.76   0.446     -.001694    .0038443
          wb_ausl |  -.0006297   .0006031    -1.04   0.297    -.0018141    .0005546
         wb_18t24 |   .0008372   .0012199     0.69   0.493    -.0015585     .003233
         wb_25t34 |   .0010792   .0008698     1.24   0.215    -.0006288    .0027873
         wb_35t44 |    .000105   .0010787     0.10   0.922    -.0020133    .0022233
         wb_45t59 |   .0007029   .0009851     0.71   0.476    -.0012317    .0026376
          avg_dur |  -.0005333   .0010384    -0.51   0.608    -.0025725     .001506
          hh_kids |   .0048196   .0018786     2.57   0.011     .0011304    .0085087
mpreis_flats_rent |   .0002245   .0012683     0.18   0.860    -.0022661    .0027151
            _cons |   1.240987   .5135375     2.42   0.016     .2324934     2.24948
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_a2 omitted because of collinearity
note: L7event_a2 omitted because of collinearity
note: F1event_a1 omitted because of collinearity
note: F1event_a2 omitted because of collinearity
note: F1event_b2 omitted because of collinearity
note: F1event_b1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  68,    617) =      16.15
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9738
                                                  Adj R-squared   =     0.9677
                                                  Within R-sq.    =     0.2226
Number of clusters (sb_new)  =        618         Root MSE        =     1.6509

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_a1 |   .5504174   .7071671     0.78   0.437    -.8383289    1.939164
       F6event_a1 |   .0255865   .5963103     0.04   0.966    -1.145457     1.19663
       F5event_a1 |    .590011    .439166     1.34   0.180    -.2724303    1.452452
       F4event_a1 |   .1095471   .2872294     0.38   0.703    -.4545186    .6736129
       F3event_a1 |  -.0277544   .2973043    -0.09   0.926    -.6116055    .5560967
       F2event_a1 |  -.1585224   .2179257    -0.73   0.467    -.5864883    .2694436
       L0event_a1 |   1.100431   .4280028     2.57   0.010     .2599118     1.94095
       L1event_a1 |   .7820959    .384925     2.03   0.043     .0261739    1.538018
       L2event_a1 |   .9177421   .3939669     2.33   0.020     .1440634    1.691421
       L3event_a1 |     1.0799   .4036567     2.68   0.008     .2871928    1.872608
       L4event_a1 |    .674493   .7240987     0.93   0.352    -.7475038     2.09649
       L5event_a1 |    .805394   1.259866     0.64   0.523    -1.668752     3.27954
       L6event_a1 |   3.444322   1.126112     3.06   0.002     1.232845    5.655799
       L7event_a1 |   .8396437   .6328478     1.33   0.185    -.4031531    2.082441
       F7event_a2 |  -.0827157   .5661246    -0.15   0.884     -1.19448    1.029049
       F6event_a2 |  -.1929549   .5705037    -0.34   0.735    -1.313319    .9274096
       F5event_a2 |   .2994913   .4877526     0.61   0.539    -.6583652    1.257348
       F4event_a2 |  -.1486279   .3251752    -0.46   0.648    -.7872122    .4899564
       F3event_a2 |  -.3882476    .276643    -1.40   0.161    -.9315236    .1550285
       F2event_a2 |  -.1416989   .2127095    -0.67   0.506    -.5594213    .2760236
       L0event_a2 |  -.4871444   .3737029    -1.30   0.193    -1.221028    .2467394
       L1event_a2 |  -.4479547   .4284055    -1.05   0.296    -1.289264     .393355
       L2event_a2 |  -1.081662    .378298    -2.86   0.004    -1.824569   -.3387539
       L3event_a2 |   .2378961   .3747158     0.63   0.526     -.497977    .9737691
       L4event_a2 |  -1.127701   .4564949    -2.47   0.014    -2.024173   -.2312294
       L5event_a2 |   .2496153   .4160444     0.60   0.549    -.5674194     1.06665
       L6event_a2 |          0  (omitted)
       L7event_a2 |          0  (omitted)
       F7event_b2 |  -1.035074   .6145789    -1.68   0.093    -2.241994    .1718465
       F6event_b2 |  -.1480525   .4470505    -0.33   0.741    -1.025977    .7298725
       F5event_b2 |   .0205925   .3772847     0.05   0.956    -.7203254    .7615104
       F4event_b2 |  -.0532281   .2720099    -0.20   0.845    -.5874055    .4809493
       F3event_b2 |  -.2035065   .2786973    -0.73   0.466    -.7508167    .3438038
       F2event_b2 |    .249207   .2008456     1.24   0.215    -.1452169    .6436308
       L0event_b2 |  -1.958892   .4055427    -4.83   0.000    -2.755303    -1.16248
       L1event_b2 |  -1.926831   .3679124    -5.24   0.000    -2.649343   -1.204318
       L2event_b2 |  -1.281757   .4931889    -2.60   0.010    -2.250289   -.3132243
       L3event_b2 |   -1.10444    .553895    -1.99   0.047    -2.192189   -.0166924
       L4event_b2 |  -1.016511    .702352    -1.45   0.148    -2.395801    .3627796
       L5event_b2 |  -2.199723   .5749577    -3.83   0.000    -3.328835   -1.070612
       L6event_b2 |  -.0601119    .582582    -0.10   0.918    -1.204196    1.083972
       L7event_b2 |    3.53991   .6028417     5.87   0.000     2.356039     4.72378
       F7event_b1 |   7.08e-06    .652504     0.00   1.000    -1.281391    1.281405
       F6event_b1 |   .4565276   .6002743     0.76   0.447    -.7223008    1.635356
       F5event_b1 |   .0972366   .4761603     0.20   0.838    -.8378549    1.032328
       F4event_b1 |   .1605574   .3223197     0.50   0.619    -.4724193    .7935341
       F3event_b1 |   .3951251    .323666     1.22   0.223    -.2404956    1.030746
       F2event_b1 |    .124705   .2396604     0.52   0.603     -.345944    .5953539
       L0event_b1 |  -2.571843    .371148    -6.93   0.000     -3.30071   -1.842977
       L1event_b1 |  -2.255959   .4578531    -4.93   0.000    -3.155098    -1.35682
       L2event_b1 |  -1.911936   .4811171    -3.97   0.000    -2.856762   -.9671105
       L3event_b1 |  -1.560785   .4278063    -3.65   0.000    -2.400918   -.7206521
       L4event_b1 |  -2.038039   .9291662    -2.19   0.029    -3.862751   -.2133273
       L5event_b1 |  -.9651406    .892016    -1.08   0.280    -2.716896    .7866148
       L6event_b1 |  -1.138818    .773325    -1.47   0.141    -2.657486    .3798505
       L7event_b1 |  -.4990939   .6198834    -0.81   0.421    -1.716431    .7182433
       F1event_a1 |          0  (omitted)
       F1event_a2 |          0  (omitted)
       F1event_b2 |          0  (omitted)
       F1event_b1 |          0  (omitted)
        ln_ew_ges |  -.9260985   .9146045    -1.01   0.312    -2.722214    .8700168
         ew_biodt |   .3698315   .0277716    13.32   0.000     .3152932    .4243699
        ew_dtmihi |   .0640185   .0504605     1.27   0.205    -.0350767    .1631138
         ew_ledig |   .2243906   .0548337     4.09   0.000     .1167074    .3320739
       ew_married |   .4167514   .0559543     7.45   0.000     .3068674    .5266355
        wb_anteil |  -.2853126   .0197764   -14.43   0.000    -.3241499   -.2464753
          wb_ausl |   .0179548   .0157968     1.14   0.256    -.0130672    .0489768
         wb_18t24 |  -.0144846   .0300744    -0.48   0.630    -.0735452     .044576
         wb_25t34 |   -.067088   .0191103    -3.51   0.000    -.1046172   -.0295589
         wb_35t44 |   .0005682   .0222572     0.03   0.980    -.0431409    .0442774
         wb_45t59 |   .0122536    .021551     0.57   0.570    -.0300686    .0545757
          avg_dur |   -.022343   .0209007    -1.07   0.285    -.0633882    .0187022
          hh_kids |  -.0456284   .0399784    -1.14   0.254    -.1241386    .0328818
mpreis_flats_rent |    .028691   .0243427     1.18   0.239    -.0191136    .0764957
            _cons |   12.11559   8.599652     1.41   0.159    -4.772548    29.00373
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_a2 omitted because of collinearity
note: L7event_a2 omitted because of collinearity
note: F1event_a1 omitted because of collinearity
note: F1event_a2 omitted because of collinearity
note: F1event_b2 omitted because of collinearity
note: F1event_b1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  68,    617) =      22.30
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9634
                                                  Adj R-squared   =     0.9549
                                                  Within R-sq.    =     0.2418
Number of clusters (sb_new)  =        618         Root MSE        =     1.6529

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_a1 |  -1.492807   .6991619    -2.14   0.033    -2.865833   -.1197814
       F6event_a1 |   -.448995   .6139298    -0.73   0.465     -1.65464    .7566503
       F5event_a1 |  -1.863921    .493733    -3.78   0.000    -2.833522   -.8943203
       F4event_a1 |  -.4871234   .2796536    -1.74   0.082    -1.036312    .0620649
       F3event_a1 |   .0737718   .2577265     0.29   0.775    -.4323558    .5798993
       F2event_a1 |  -.1746167   .1973979    -0.88   0.377    -.5622699    .2130365
       L0event_a1 |  -.7409753   .3993307    -1.86   0.064    -1.525187    .0432368
       L1event_a1 |  -.5647613   .3655631    -1.54   0.123     -1.28266    .1531376
       L2event_a1 |  -.3547099   .4050149    -0.88   0.381    -1.150085    .4406649
       L3event_a1 |  -1.020095    .359123    -2.84   0.005    -1.725347   -.3148438
       L4event_a1 |  -.5074852   1.132936    -0.45   0.654    -2.732364    1.717394
       L5event_a1 |   1.499084   .9933098     1.51   0.132    -.4515943    3.449761
       L6event_a1 |  -3.126453   .7530393    -4.15   0.000    -4.605284   -1.647623
       L7event_a1 |  -2.060461   .7081124    -2.91   0.004    -3.451063   -.6698583
       F7event_a2 |    -.05831   .4078318    -0.14   0.886    -.8592167    .7425968
       F6event_a2 |   .2854288   .3677776     0.78   0.438    -.4368188    1.007676
       F5event_a2 |  -.7405715   .4637571    -1.60   0.111    -1.651305    .1701622
       F4event_a2 |  -.1726349   .2697772    -0.64   0.522    -.7024277    .3571579
       F3event_a2 |   .5282463   .2666653     1.98   0.048     .0045646    1.051928
       F2event_a2 |   .0503322   .2140392     0.24   0.814    -.3700014    .4706659
       L0event_a2 |   .0291633   .3598288     0.08   0.935    -.6774744    .7358011
       L1event_a2 |   .4356636   .4251104     1.02   0.306    -.3991752    1.270502
       L2event_a2 |   .9399731   .4644165     2.02   0.043     .0279444    1.852002
       L3event_a2 |  -.1186157   .4886629    -0.24   0.808     -1.07826    .8410286
       L4event_a2 |   2.168899   1.038573     2.09   0.037     .1293332    4.208465
       L5event_a2 |   .4822114   .4481097     1.08   0.282    -.3977937    1.362217
       L6event_a2 |          0  (omitted)
       L7event_a2 |          0  (omitted)
       F7event_b2 |   .9297172   .4019551     2.31   0.021     .1403513    1.719083
       F6event_b2 |   .4130476   .3354022     1.23   0.219    -.2456208    1.071716
       F5event_b2 |  -.0161236   .4018115    -0.04   0.968    -.8052075    .7729602
       F4event_b2 |  -.1017406    .275778    -0.37   0.712     -.643318    .4398368
       F3event_b2 |  -.2603043   .2790075    -0.93   0.351    -.8082238    .2876152
       F2event_b2 |   -.242882   .2051819    -1.18   0.237    -.6458216    .1600576
       L0event_b2 |   1.100357   .3232045     3.40   0.001     .4656424    1.735071
       L1event_b2 |      1.644    .356824     4.61   0.000     .9432637    2.344737
       L2event_b2 |   1.243435   .4627352     2.69   0.007     .3347078    2.152162
       L3event_b2 |   1.009515     .50161     2.01   0.045     .0244447    1.994584
       L4event_b2 |   1.952037   .4279615     4.56   0.000     1.111599    2.792475
       L5event_b2 |   3.193558   .4237297     7.54   0.000      2.36143    4.025685
       L6event_b2 |   .7110064   .5431344     1.31   0.191    -.3556098    1.777623
       L7event_b2 |   -1.32864   .6105151    -2.18   0.030     -2.52758   -.1297008
       F7event_b1 |    .834685   .5314788     1.57   0.117    -.2090418    1.878412
       F6event_b1 |   .4081068   .4350119     0.94   0.349    -.4461766     1.26239
       F5event_b1 |   .2086756   .4447347     0.47   0.639    -.6647017    1.082053
       F4event_b1 |  -.1943066   .2603453    -0.75   0.456     -.705577    .3169638
       F3event_b1 |  -.3289186   .2479094    -1.33   0.185    -.8157671      .15793
       F2event_b1 |   .0332513   .2495983     0.13   0.894    -.4569139    .5234165
       L0event_b1 |    2.00773   .3836318     5.23   0.000     1.254347    2.761112
       L1event_b1 |   2.344847   .4319353     5.43   0.000     1.496606    3.193089
       L2event_b1 |   2.558116    .490829     5.21   0.000     1.594218    3.522014
       L3event_b1 |   2.158409   .4554482     4.74   0.000     1.263992    3.052825
       L4event_b1 |   3.517183   .5857647     6.00   0.000     2.366849    4.667517
       L5event_b1 |   3.178977   .7994335     3.98   0.000     1.609037    4.748917
       L6event_b1 |   3.130183   .6336071     4.94   0.000     1.885895    4.374471
       L7event_b1 |   2.901058   .6276351     4.62   0.000     1.668498    4.133617
       F1event_a1 |          0  (omitted)
       F1event_a2 |          0  (omitted)
       F1event_b2 |          0  (omitted)
       F1event_b1 |          0  (omitted)
        ln_ew_ges |     2.6501   1.304053     2.03   0.043     .0891799     5.21102
         ew_biodt |   .3933819   .0282618    13.92   0.000      .337881    .4488829
        ew_dtmihi |  -.2309335   .0594544    -3.88   0.000    -.3476911   -.1141759
         ew_ledig |   .1992847   .0776039     2.57   0.010     .0468849    .3516844
       ew_married |   .2180018   .0777526     2.80   0.005       .06531    .3706936
        wb_anteil |  -.2460919   .0213508   -11.53   0.000    -.2880209   -.2041629
          wb_ausl |  -.0688064   .0145408    -4.73   0.000    -.0973619   -.0402509
         wb_18t24 |  -.0315868   .0275482    -1.15   0.252    -.0856864    .0225128
         wb_25t34 |    .047318   .0193396     2.45   0.015     .0093385    .0852975
         wb_35t44 |  -.0052067   .0239998    -0.22   0.828    -.0523379    .0419246
         wb_45t59 |  -.0360557    .020405    -1.77   0.078    -.0761273     .004016
          avg_dur |   .0394441     .02282     1.73   0.084    -.0053701    .0842583
          hh_kids |  -.0715763   .0414459    -1.73   0.085    -.1529685    .0098159
mpreis_flats_rent |  -.0172704   .0235116    -0.73   0.463    -.0634429    .0289021
            _cons |  -12.69852   10.88231    -1.17   0.244    -34.06938    8.672341
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_a2 omitted because of collinearity
note: L7event_a2 omitted because of collinearity
note: F1event_a1 omitted because of collinearity
note: F1event_a2 omitted because of collinearity
note: F1event_b2 omitted because of collinearity
note: F1event_b1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  68,    617) =      27.25
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9905
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4473
Number of clusters (sb_new)  =        618         Root MSE        =     1.6218

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_a1 |  -.9423918   .4414276    -2.13   0.033    -1.809274    -.075509
       F6event_a1 |  -.4234091   .3700781    -1.14   0.253    -1.150175    .3033563
       F5event_a1 |   -1.27391   .4688001    -2.72   0.007    -2.194547   -.3532726
       F4event_a1 |  -.3775766   .2895136    -1.30   0.193    -.9461281    .1909749
       F3event_a1 |   .0460171   .2819482     0.16   0.870    -.5076775    .5997116
       F2event_a1 |  -.3331387   .2587532    -1.29   0.198    -.8412824     .175005
       L0event_a1 |   .3594548   .2704671     1.33   0.184    -.1716928    .8906024
       L1event_a1 |   .2173348   .3129027     0.69   0.488    -.3971486    .8318182
       L2event_a1 |   .5630326   .3442103     1.64   0.102    -.1129331    1.238998
       L3event_a1 |   .0598049   .3050322     0.20   0.845    -.5392222    .6588321
       L4event_a1 |   .1670089   1.018555     0.16   0.870    -1.833246    2.167264
       L5event_a1 |   2.304478    1.56027     1.48   0.140    -.7596062    5.368561
       L6event_a1 |   .3178684   1.349453     0.24   0.814    -2.332209    2.967946
       L7event_a1 |  -1.220815   .8515037    -1.43   0.152    -2.893012    .4513816
       F7event_a2 |  -.1410263   .5763859    -0.24   0.807    -1.272942    .9908897
       F6event_a2 |   .0924719   .5029071     0.18   0.854    -.8951453    1.080089
       F5event_a2 |  -.4410798   .5442502    -0.81   0.418    -1.509887    .6277275
       F4event_a2 |  -.3212624   .2914663    -1.10   0.271    -.8936487    .2511239
       F3event_a2 |   .1399978   .2748694     0.51   0.611    -.3997952    .6797908
       F2event_a2 |  -.0913662   .2473794    -0.37   0.712    -.5771739    .3944414
       L0event_a2 |  -.4579806   .2918825    -1.57   0.117    -1.031184    .1152231
       L1event_a2 |  -.0122908   .4423255    -0.03   0.978    -.8809368    .8563551
       L2event_a2 |  -.1416891   .4282166    -0.33   0.741    -.9826277    .6992496
       L3event_a2 |   .1192811   .5238735     0.23   0.820    -.9095103    1.148072
       L4event_a2 |   1.041199   1.000409     1.04   0.298    -.9234193    3.005818
       L5event_a2 |   .7318298   .5543247     1.32   0.187    -.3567621    1.820422
       L6event_a2 |          0  (omitted)
       L7event_a2 |          0  (omitted)
       F7event_b2 |  -.1053546     .63639    -0.17   0.869    -1.355108    1.144399
       F6event_b2 |   .2649948   .4408227     0.60   0.548    -.6007001     1.13069
       F5event_b2 |   .0044692   .3803655     0.01   0.991    -.7424988    .7514372
       F4event_b2 |  -.1549681   .2893036    -0.54   0.592    -.7231073    .4131711
       F3event_b2 |  -.4638103   .2385986    -1.94   0.052    -.9323741    .0047535
       F2event_b2 |   .0063255   .2211718     0.03   0.977    -.4280153    .4406663
       L0event_b2 |  -.8585342   .3240226    -2.65   0.008    -1.494855   -.2222134
       L1event_b2 |    -.28283   .3902753    -0.72   0.469    -1.049259     .483599
       L2event_b2 |  -.0383228   .4310277    -0.09   0.929    -.8847821    .8081364
       L3event_b2 |   -.094926   .4505288    -0.21   0.833    -.9796817    .7898297
       L4event_b2 |   .9355229   .7792826     1.20   0.230     -.594845    2.465891
       L5event_b2 |    .993835    .529964     1.88   0.061    -.0469169    2.034587
       L6event_b2 |   .6508898   .6188715     1.05   0.293    -.5644602     1.86624
       L7event_b2 |    2.21127   .5518728     4.01   0.000     1.127494    3.295047
       F7event_b1 |   .8346925   .4269132     1.96   0.051    -.0036866    1.673072
       F6event_b1 |   .8646344   .5147683     1.68   0.094    -.1462759    1.875545
       F5event_b1 |   .3059126   .3670592     0.83   0.405    -.4149242    1.026749
       F4event_b1 |  -.0337487   .2926026    -0.12   0.908    -.6083664     .540869
       F3event_b1 |   .0662063   .3020509     0.22   0.827    -.5269662    .6593788
       F2event_b1 |   .1579568   .2463538     0.64   0.522    -.3258369    .6417504
       L0event_b1 |  -.5641129   .2932569    -1.92   0.055    -1.140016    .0117897
       L1event_b1 |    .088889   .3158498     0.28   0.778    -.5313821      .70916
       L2event_b1 |   .6461809   .3837524     1.68   0.093    -.1074383      1.3998
       L3event_b1 |   .5976251   .4137285     1.44   0.149    -.2148616    1.410112
       L4event_b1 |   1.479144   .7467415     1.98   0.048     .0126813    2.945608
       L5event_b1 |   2.213835    .865931     2.56   0.011     .5133057    3.914364
       L6event_b1 |   1.991363   .6590998     3.02   0.003     .6970125    3.285714
       L7event_b1 |   2.401963   .6674855     3.60   0.000     1.091144    3.712782
       F1event_a1 |          0  (omitted)
       F1event_a2 |          0  (omitted)
       F1event_b2 |          0  (omitted)
       F1event_b1 |          0  (omitted)
        ln_ew_ges |   1.724002   1.075598     1.60   0.109    -.3882756    3.836279
         ew_biodt |   .7632135   .0316828    24.09   0.000     .7009943    .8254327
        ew_dtmihi |  -.1669148   .0521771    -3.20   0.001     -.269381   -.0644486
         ew_ledig |   .4236755   .0719608     5.89   0.000     .2823578    .5649932
       ew_married |   .6347533   .0698493     9.09   0.000      .497582    .7719246
        wb_anteil |  -.5314045   .0241447   -22.01   0.000    -.5788202   -.4839888
          wb_ausl |  -.0508516   .0176023    -2.89   0.004    -.0854193   -.0162839
         wb_18t24 |  -.0460714   .0264237    -1.74   0.082    -.0979627    .0058198
         wb_25t34 |    -.01977   .0167038    -1.18   0.237    -.0525733    .0130333
         wb_35t44 |  -.0046384   .0210429    -0.22   0.826    -.0459627    .0366859
         wb_45t59 |  -.0238021   .0194807    -1.22   0.222    -.0620585    .0144544
          avg_dur |   .0171011   .0222801     0.77   0.443    -.0266528    .0608551
          hh_kids |  -.1172048   .0359955    -3.26   0.001    -.1878933   -.0465163
mpreis_flats_rent |   .0114207   .0235625     0.48   0.628    -.0348518    .0576931
            _cons |  -.5829435   10.01515    -0.06   0.954    -20.25085    19.08496
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
.         * PLOT: FIGURE D10. Effect Heterogeneity by Share of Addresses with Distance Increase
.         grc1leg del_street_dist turnout_urne turnout_pos_req turnout_tot_req, col(2)  xcommon po
> s(6) imargins(small)

.         gr_edit .plotregion1.graph1.yaxis1.reset_rule -0.6 0.6 .3 , tickset(major) ruletype(rang
> e) 

.         gr_edit .plotregion1.graph1.yaxis1.title.text = {}

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"Change in distance in km"'

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"(estimates)"'

.         gr_edit .style.editstyle declared_ysize(4) editcopy

. 
.         graph export "$figures/Figure_D10_ES_het_by_distance_shrHHdist4.pdf", replace   
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D10_ES
    > _het_by_distance_shrHHdist4.pdf saved as PDF format

. 
end of do-file
Running: 04b_rob_het_by_distance_no_ambiguity_figure_d9.do

. 
. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output: Figure D.9      
> 
> Task: Heterogeneity by distance change, where changes are consistent for at least
>                 90% of addresses or polling locations barely changed
> 
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
. ********************************************************************************
. *        Prep Estimation *
. ********************************************************************************
. 
.         // relabel outcomes 
.         lab var turnout_urne    "Polling Place Turnout"

.         lab var turnout_pos_req "Mail-in Turnout"

.         lab var turnout_tot_req "Total Turnout" 

.         
. // DEFINE sample, where treatm changed distance in SAME direction for >90% of units
.         gen smpl_ambi=0

.         gen frac_inc = street_increased/treat_simple                    // share del_street_dist
>  >0
(3,872 missing values generated)

.         gen frac_dec = street_decreased/treat_simple                    // share del_street_dist
>  <0
(3,872 missing values generated)

.         gen d_in  = (frac_inc>.90 | frac_dec>.90) if K==0               // I(shares > 90%)
(4,664 missing values generated)

.         bys sb_new (d_in): replace smpl_ambi=d_in[1]
(3728 real changes made, 2704 to missing)

.         replace smpl_ambi = 1 if missing(Ei)
(2,704 real changes made)

.         lab var smpl_ambi "sample def excluding units with ambiguous distance change for HHs wit
> hin precinct"
note: label truncated to 80 characters

.         
.         // compute dummies for DISTANCE increase/decrease 90%+ AND 3rd cat. for dist(WL_t, WL_t-
> 1) small
.         cap drop tmp*

.         gen     tmp = (del_street_dist>0 & d_in==1)                     if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase 90%+"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0 & d_in==1)                     if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease 90%+"

.         
.         cap drop tmp*

.         gen     tmp = (wl_street_dist<.8 & d_in==0)                     if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_sm = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_sm = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_sm "=1 if dist changed little" 

.         
.         tab ind_dist_up ind_dist_dn  if K==0    

=1 if dist |  =1 if dist decrease
  increase |         90%+
      90%+ |         0          1 |     Total
-----------+----------------------+----------
         0 |       152         39 |       191 
         1 |        89          0 |        89 
-----------+----------------------+----------
     Total |       241         39 |       280 

.         tab ind_dist_sm if K==0

 =1 if dist |
    changed |
     little |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        208       74.29       74.29
          1 |         72       25.71      100.00
------------+-----------------------------------
      Total |        280      100.00

.         assert ind_dist_sm==0 if ind_dist_up==1| ind_dist_dn==1

.         
.         *>>> Adj. sample to include SMALL dist changes<<<
.         replace smpl_ambi = 1 if ind_dist_sm==1
(576 real changes made)

. 
.                 
. ********************************************************************************
. * Het: 3 groups, 90%+ HH dist UP, 90%# HH DOWN, dist[WL_t, WL_(t-1)]=small (Figure D9)
. ********************************************************************************                
>         
.         
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         
.         // Create two set of dummies: Reason Dummy x rel. time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_dn    // a := decrease
  3.                 gen             F`l'event_b = F`l'event *ind_dist_sm    // b:= middle
  4.                 gen     F`l'event_c = F`l'event *ind_dist_up    // c := increase
  5.                 assert  F`l'event_b+F`l'event_a+F`l'event_c==F`l'event  if smpl_ambi==1 
  6.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  7.                 lab var F`l'event_b "(N0)x\hspace{.7cm}Reassignment (#t-`l'#)"
  8.                 lab var F`l'event_c "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  9.                 
.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_dn    // a := decrease
  3.                 gen             L`l'event_b = L`l'event *ind_dist_sm    // b:= middle
  4.                 gen     L`l'event_c = L`l'event *ind_dist_up    // c := increase
  5.                 assert  L`l'event_b+L`l'event_a+L`l'event_c==L`l'event  if smpl_ambi==1 
  6.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  7.                 lab var L`l'event_b "(N0)x\hspace{.7cm}Reassignment (#t+`l'#)"
  8.                 lab var L`l'event_c "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"          
  9.                 
.         }               

.         
.         // ORDER dummies
.         order *event_b, last

.         order *event_c, last

.         order F1event*,last     

.         // Label for figure header
.         lab var turnout_urne    "{bf:Panel B.} Effect on Polling Place Turnout"

.         lab var turnout_pos_req "{bf:Panel C.} Effect on Mail-in Turnout"

.         lab var turnout_tot_req "{bf:Panel D.} Effect on Total Turnout"

.         lab var del_street_dist "{bf:Panel A.} Change in Distance"      

.         
.         
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         outreg, clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req del_street_dist treat_
> simple{
  2.         
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F7event_c-L7event_c F1event
> _a F1event_b F1event_c ///
>                                 $ctr $wgt if smpl_trim ==1 & smpl_ambi==1, absorb(i.wahl_id#i.st
> adtbez i.sb_new) cluster(sb_new)                
  3.                                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2even
> t_b F4event_c-L2event_c) store(`v')
  4. 
.                 estimates store `v'_a
  5.                 estimates store `v'_b   
  6.                 estimates store `v'_c   
  7. 
.                 
.                 // PLOT 
.                 event_plot  `v'_a `v'_b `v'_c, ///
>                 stub_lag(L#event_a L#event_b L#event_c ) stub_lead(F#event_a F#event_b F#event_c
> ) plottype(scatter) ciplottype(rcap) ///
>                 together perturb(-0.15(0.15)0.15) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(small)) xlab
> el(-4(1)2) xtitle("Election since reassignment") ///
>                         legend(pos(12) order(1 "Distance decrease for >90% addresses" 3 "Polling
>  location moved <800m" 5 "Distance increase for >90% addresses" ) row(1) region(style(none)) bex
> pand span justification(left)) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lpat(solid) lcol(gra
> y)) ylabel(, angle(horizontal)) ///
>                         title("`:var lab `v''", nobox span bexpand justification(left) size(medi
> um)) ///
>                         name(`v', replace)) ///
>                 lag_opt1(msymbol(O) msize(2.5pt) color(black))  lag_ci_opt1(color(black)) ///
>                 lag_opt2(msymbol(Dh) msize(2.5pt) color(gray)) lag_ci_opt2(color(gray)) ///
>                 lag_opt3(msymbol(S) msize(2.5pt) color(cranberry)) lag_ci_opt3(color(cranberry))
>                                 
  8. 
.         }
(MWFE estimator converged in 8 iterations)
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,106
Absorbing 2 HDFE groups                           F(  54,    537) =      10.96
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9743
                                                  Adj R-squared   =     0.9682
                                                  Within R-sq.    =     0.2050
Number of clusters (sb_new)  =        538         Root MSE        =     1.6498

                                    (Std. err. adjusted for 538 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .2584634   1.364369     0.19   0.850    -2.421692    2.938619
        F6event_a |  -.0614768   .9110214    -0.07   0.946     -1.85108    1.728126
        F5event_a |   .8795185   .7459542     1.18   0.239    -.5858276    2.344865
        F4event_a |   .1756662   .4731717     0.37   0.711    -.7538282    1.105161
        F3event_a |   .0511071   .4517977     0.11   0.910    -.8364003    .9386146
        F2event_a |   .0081721   .3269967     0.02   0.980    -.6341774    .6505217
        L0event_a |   1.553144   .6537605     2.38   0.018     .2689022    2.837385
        L1event_a |   1.177002   .5711806     2.06   0.040     .0549799    2.299024
        L2event_a |   .9370255   .5647829     1.66   0.098    -.1724292     2.04648
        L3event_a |   1.218011   .5578074     2.18   0.029     .1222586    2.313763
        L4event_a |   1.257952   .7406323     1.70   0.090    -.1969395    2.712844
        L5event_a |   2.170252    .957407     2.27   0.024     .2895296    4.050974
        L6event_a |   4.073106   1.146961     3.55   0.000     1.820025    6.326187
        L7event_a |   1.428192   .7175631     1.99   0.047     .0186175    2.837767
        F7event_b |  -.5872655   .5847369    -1.00   0.316    -1.735918    .5613866
        F6event_b |  -.1823328   .4346776    -0.42   0.675     -1.03621    .6715443
        F5event_b |  -.0597411   .4017249    -0.15   0.882     -.848886    .7294038
        F4event_b |  -.2150308   .2315394    -0.93   0.353    -.6698648    .2398033
        F3event_b |  -.2167129   .2507081    -0.86   0.388    -.7092018    .2757759
        F2event_b |   -.176911   .2092666    -0.85   0.398    -.5879926    .2341706
        L0event_b |  -.4553005   .3644231    -1.25   0.212     -1.17117    .2605692
        L1event_b |  -.6360675   .3546851    -1.79   0.073    -1.332808    .0606729
        L2event_b |  -.6294619    .420703    -1.50   0.135    -1.455887    .1969635
        L3event_b |  -.4169598   .4098788    -1.02   0.309    -1.222122    .3882026
        L4event_b |  -1.138246   .8986605    -1.27   0.206    -2.903567    .6270747
        L5event_b |  -1.973727   .9655923    -2.04   0.041    -3.870528   -.0769255
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |  -.0216992    .508531    -0.04   0.966    -1.020653    .9772547
        F6event_c |   .4104514   .4667093     0.88   0.380    -.5063484    1.327251
        F5event_c |   .1092808     .40467     0.27   0.787    -.6856495    .9042111
        F4event_c |   .1623825   .2943236     0.55   0.581    -.4157842    .7405493
        F3event_c |   .3743278   .2951827     1.27   0.205    -.2055264    .9541821
        F2event_c |   .3310957   .2098287     1.58   0.115    -.0810901    .7432815
        L0event_c |  -2.158944   .3604642    -5.99   0.000    -2.867037   -1.450851
        L1event_c |  -2.049851   .4132843    -4.96   0.000    -2.861703   -1.237999
        L2event_c |  -1.739972   .4404978    -3.95   0.000    -2.605282   -.8746616
        L3event_c |  -1.420295   .3874458    -3.67   0.000     -2.18139   -.6591998
        L4event_c |  -1.932444    .880413    -2.19   0.029     -3.66192   -.2029686
        L5event_c |  -.8234797   .8355592    -0.99   0.325    -2.464845    .8178857
        L6event_c |  -.6926505   .8300547    -0.83   0.404    -2.323203    .9379018
        L7event_c |  -.3005587   .6309474    -0.48   0.634    -1.539986     .938869
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -1.380373    .972184    -1.42   0.156    -3.290123    .5293766
         ew_biodt |   .3700666   .0287084    12.89   0.000      .313672    .4264611
        ew_dtmihi |   .0645413   .0549597     1.17   0.241    -.0434211    .1725038
         ew_ledig |   .2349055   .0649726     3.62   0.000     .1072738    .3625372
       ew_married |   .3868298   .0657933     5.88   0.000      .257586    .5160736
        wb_anteil |  -.2932149   .0211316   -13.88   0.000    -.3347256   -.2517042
          wb_ausl |   .0179348   .0167514     1.07   0.285    -.0149714     .050841
         wb_18t24 |  -.0280233   .0329108    -0.85   0.395    -.0926731    .0366265
         wb_25t34 |  -.0616814   .0204895    -3.01   0.003    -.1019308   -.0214321
         wb_35t44 |   .0000219   .0236416     0.00   0.999    -.0464196    .0464633
         wb_45t59 |   .0106549   .0232219     0.46   0.647     -.034962    .0562718
          avg_dur |  -.0240743   .0223902    -1.08   0.283    -.0680573    .0199088
          hh_kids |  -.0244115   .0424026    -0.58   0.565    -.1077068    .0588839
mpreis_flats_rent |   .0335253   .0272086     1.23   0.218     -.019923    .0869735
            _cons |   16.44784   9.144341     1.80   0.073    -1.515226     34.4109
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       538         538           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,106
Absorbing 2 HDFE groups                           F(  54,    537) =      12.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9625
                                                  Adj R-squared   =     0.9535
                                                  Within R-sq.    =     0.2378
Number of clusters (sb_new)  =        538         Root MSE        =     1.6578

                                    (Std. err. adjusted for 538 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -1.173325   1.251334    -0.94   0.349    -3.631434    1.284784
        F6event_a |  -.1725551   .9320545    -0.19   0.853    -2.003475    1.658365
        F5event_a |  -1.613108   .6779643    -2.38   0.018    -2.944896   -.2813211
        F4event_a |   -.304631   .3786803    -0.80   0.421    -1.048507    .4392455
        F3event_a |   .3417048   .3665493     0.93   0.352    -.3783416    1.061751
        F2event_a |   .1055721   .2577158     0.41   0.682    -.4006826    .6118268
        L0event_a |  -.7846805   .6209515    -1.26   0.207    -2.004472    .4351114
        L1event_a |  -.6865283   .5505149    -1.25   0.213    -1.767955    .3948986
        L2event_a |  -.2896031   .5286662    -0.55   0.584     -1.32811    .7489043
        L3event_a |  -.5395889   .4141795    -1.30   0.193      -1.3532    .2740218
        L4event_a |  -.8219434   1.148601    -0.72   0.475    -3.078245    1.434358
        L5event_a |   1.166863   1.205681     0.97   0.334    -1.201567    3.535292
        L6event_a |  -3.132043   .7954425    -3.94   0.000    -4.694603   -1.569483
        L7event_a |  -2.250005   .8335953    -2.70   0.007    -3.887512   -.6124973
        F7event_b |   -.133327   .5357284    -0.25   0.804    -1.185707    .9190533
        F6event_b |   .3318803   .4021832     0.83   0.410    -.4581649    1.121926
        F5event_b |  -1.007951   .4418488    -2.28   0.023    -1.875915   -.1399867
        F4event_b |  -.3542578   .2701863    -1.31   0.190    -.8850095     .176494
        F3event_b |   .0099601   .2594203     0.04   0.969    -.4996429    .5195631
        F2event_b |    -.14759   .1818401    -0.81   0.417    -.5047951    .2096152
        L0event_b |  -.2111483   .3331535    -0.63   0.526    -.8655922    .4432955
        L1event_b |   .1061606   .3578079     0.30   0.767    -.5967142    .8090355
        L2event_b |   .4186577   .4708152     0.89   0.374    -.5062076    1.343523
        L3event_b |    -.21212    .452163    -0.47   0.639    -1.100345    .6761052
        L4event_b |   1.837904   .8292698     2.22   0.027      .208894    3.466915
        L5event_b |   2.065643   .9447909     2.19   0.029     .2097036    3.921582
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |   1.031073   .4387129     2.35   0.019     .1692693    1.892877
        F6event_c |   .4323601    .353598     1.22   0.222    -.2622449    1.126965
        F5event_c |   .2227011   .4053344     0.55   0.583    -.5735343    1.018936
        F4event_c |  -.0622633   .2583748    -0.24   0.810    -.5698125     .445286
        F3event_c |  -.3205515    .239811    -1.34   0.182    -.7916343    .1505312
        F2event_c |  -.1430973   .2220407    -0.64   0.520    -.5792722    .2930775
        L0event_c |   1.614575   .3467006     4.66   0.000     .9335197    2.295631
        L1event_c |   2.268031   .3588954     6.32   0.000      1.56302    2.973042
        L2event_c |   2.405229   .4398786     5.47   0.000     1.541136    3.269323
        L3event_c |   1.882833   .4027613     4.67   0.000     1.091652    2.674014
        L4event_c |   3.495009    .526011     6.64   0.000     2.461718    4.528301
        L5event_c |   2.973923   .7551338     3.94   0.000     1.490545    4.457302
        L6event_c |   2.856476   .6446125     4.43   0.000     1.590205    4.122747
        L7event_c |   2.516252   .5889746     4.27   0.000     1.359276    3.673229
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   2.967632   1.376909     2.16   0.032     .2628435     5.67242
         ew_biodt |   .3982022     .03004    13.26   0.000      .339192    .4572125
        ew_dtmihi |  -.2064345   .0644119    -3.20   0.001    -.3329647   -.0799043
         ew_ledig |   .2631128   .0789238     3.33   0.001     .1080756      .41815
       ew_married |   .3137398   .0783581     4.00   0.000     .1598139    .4676657
        wb_anteil |  -.2407201   .0228073   -10.55   0.000    -.2855225   -.1959177
          wb_ausl |  -.0695519   .0153612    -4.53   0.000    -.0997273   -.0393765
         wb_18t24 |  -.0069703   .0299518    -0.23   0.816    -.0658073    .0518667
         wb_25t34 |   .0430429   .0207855     2.07   0.039     .0022121    .0838738
         wb_35t44 |  -.0130633   .0258935    -0.50   0.614    -.0639283    .0378017
         wb_45t59 |  -.0466422    .021984    -2.12   0.034    -.0898274    -.003457
          avg_dur |   .0457065   .0252385     1.81   0.071    -.0038719    .0952849
          hh_kids |  -.0918158   .0444998    -2.06   0.040    -.1792309   -.0044007
mpreis_flats_rent |   -.008411   .0262662    -0.32   0.749    -.0600081    .0431861
            _cons |  -22.70048   11.56313    -1.96   0.050    -45.41499    .0140225
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       538         538           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,106
Absorbing 2 HDFE groups                           F(  54,    537) =      25.64
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9881
                                                  Within R-sq.    =     0.4479
Number of clusters (sb_new)  =        538         Root MSE        =     1.6273

                                    (Std. err. adjusted for 538 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   -.914864   .4307177    -2.12   0.034    -1.760962   -.0687659
        F6event_a |  -.2340327   .4562953    -0.51   0.608    -1.130375    .6623099
        F5event_a |  -.7335893   .6019637    -1.22   0.224    -1.916082    .4489031
        F4event_a |  -.1289649   .4312509    -0.30   0.765    -.9761104    .7181806
        F3event_a |   .3928119   .4105235     0.96   0.339    -.4136169    1.199241
        F2event_a |   .1137448   .3993253     0.28   0.776    -.6706864    .8981761
        L0event_a |   .7684624    .352139     2.18   0.030     .0767236    1.460201
        L1event_a |   .4904737   .4801523     1.02   0.307    -.4527334    1.433681
        L2event_a |   .6474231   .5136317     1.26   0.208    -.3615507    1.656397
        L3event_a |   .6784216   .4347425     1.56   0.119    -.1755828    1.532426
        L4event_a |   .4360103    1.16159     0.38   0.708    -1.845808    2.717828
        L5event_a |   3.337113   1.836485     1.82   0.070    -.2704631    6.944689
        L6event_a |   .9410628   1.172052     0.80   0.422    -1.361306    3.243432
        L7event_a |  -.8218109   1.020499    -0.81   0.421    -2.826471    1.182849
        F7event_b |  -.7205927   .4752074    -1.52   0.130    -1.654086    .2129006
        F6event_b |   .1495471   .3690761     0.41   0.685    -.5754628    .8745569
        F5event_b |  -1.067692   .4574298    -2.33   0.020    -1.966263   -.1691207
        F4event_b |  -.5692879   .2749957    -2.07   0.039    -1.109487   -.0290886
        F3event_b |  -.2067535   .2353678    -0.88   0.380    -.6691079     .255601
        F2event_b |  -.3245011   .2139352    -1.52   0.130    -.7447535    .0957513
        L0event_b |   -.666449    .286669    -2.32   0.020    -1.229579   -.1033189
        L1event_b |  -.5299064   .3380299    -1.57   0.118    -1.193929    .1341166
        L2event_b |  -.2108043   .3671479    -0.57   0.566    -.9320265    .5104179
        L3event_b |  -.6290792   .3777299    -1.67   0.096    -1.371089    .1129303
        L4event_b |   .6996565   .6957676     1.01   0.315    -.6671035    2.066416
        L5event_b |   .0919177   .5460201     0.17   0.866    -.9806795    1.164515
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |   1.009375   .3605016     2.80   0.005     .3012085    1.717541
        F6event_c |   .8428113   .4097286     2.06   0.040      .037944    1.647679
        F5event_c |    .331982   .3539811     0.94   0.349    -.3633754    1.027339
        F4event_c |   .1001197    .264137     0.38   0.705    -.4187488    .6189882
        F3event_c |    .053776    .265546     0.20   0.840    -.4678603    .5754122
        F2event_c |   .1879989   .2225271     0.84   0.399    -.2491315    .6251292
        L0event_c |  -.5443679   .2674866    -2.04   0.042    -1.069816   -.0189195
        L1event_c |   .2181801   .3137016     0.70   0.487    -.3980526    .8344127
        L2event_c |   .6652583   .3490715     1.91   0.057    -.0204548    1.350971
        L3event_c |   .4625384     .38014     1.22   0.224    -.2842054    1.209282
        L4event_c |   1.562565   .7343109     2.13   0.034     .1200912    3.005039
        L5event_c |   2.150442   .8716982     2.47   0.014     .4380855    3.862799
        L6event_c |   2.163823   .6901115     3.14   0.002     .8081743    3.519472
        L7event_c |   2.215693   .6868139     3.23   0.001     .8665211    3.564864
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |   1.587258   1.103746     1.44   0.151    -.5809319    3.755448
         ew_biodt |   .7682688   .0337813    22.74   0.000     .7019091    .8346285
        ew_dtmihi |   -.141893   .0552639    -2.57   0.011     -.250453   -.0333331
         ew_ledig |   .4980184   .0704287     7.07   0.000     .3596688     .636368
       ew_married |   .7005697   .0688004    10.18   0.000     .5654187    .8357207
        wb_anteil |   -.533935   .0261902   -20.39   0.000    -.5853828   -.4824873
          wb_ausl |  -.0516171   .0193306    -2.67   0.008    -.0895901   -.0136442
         wb_18t24 |  -.0349936   .0277852    -1.26   0.208    -.0895745    .0195874
         wb_25t34 |  -.0186385   .0175793    -1.06   0.290    -.0531712    .0158942
         wb_35t44 |  -.0130414   .0226352    -0.58   0.565    -.0575057    .0314229
         wb_45t59 |  -.0359873   .0206783    -1.74   0.082    -.0766076    .0046331
          avg_dur |   .0216323   .0237872     0.91   0.364    -.0250951    .0683597
          hh_kids |  -.1162274   .0394678    -2.94   0.003    -.1937576   -.0386971
mpreis_flats_rent |   .0251142   .0266535     0.94   0.346    -.0272436    .0774721
            _cons |  -6.252653   10.54925    -0.59   0.554    -26.97551     14.4702
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       538         538           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,106
Absorbing 2 HDFE groups                           F(  54,    537) =      14.60
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5684
                                                  Adj R-squared   =     0.4656
                                                  Within R-sq.    =     0.4757
Number of clusters (sb_new)  =        538         Root MSE        =     0.0861

                                    (Std. err. adjusted for 538 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  del_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.0269475   .0304654    -0.88   0.377    -.0867935    .0328985
        F6event_a |  -.0335074   .0240282    -1.39   0.164    -.0807082    .0136934
        F5event_a |  -.0396482   .0278089    -1.43   0.155    -.0942757    .0149793
        F4event_a |  -.0108569    .014959    -0.73   0.468    -.0402423    .0185284
        F3event_a |  -.0320007   .0190505    -1.68   0.094    -.0694234     .005422
        F2event_a |   .0104341   .0207322     0.50   0.615    -.0302921    .0511603
        L0event_a |  -.3960107   .0432255    -9.16   0.000    -.4809224    -.311099
        L1event_a |  -.0028446   .0144521    -0.20   0.844    -.0312342     .025545
        L2event_a |   .0058146   .0135889     0.43   0.669    -.0208792    .0325085
        L3event_a |   .0128741    .026188     0.49   0.623    -.0385693    .0643176
        L4event_a |  -.0017878   .0226351    -0.08   0.937     -.046252    .0426763
        L5event_a |  -.0361636   .0253363    -1.43   0.154     -.085934    .0136068
        L6event_a |  -.0406606   .0257541    -1.58   0.115    -.0912516    .0099305
        L7event_a |   -.118492   .0555343    -2.13   0.033    -.2275831    -.009401
        F7event_b |   .0059912   .0184907     0.32   0.746    -.0303318    .0423141
        F6event_b |   .0043514   .0199206     0.22   0.827    -.0347804    .0434832
        F5event_b |   .0229723   .0502593     0.46   0.648    -.0757567    .1217013
        F4event_b |    .006415   .0151877     0.42   0.673    -.0234196    .0362496
        F3event_b |    .000041   .0157155     0.00   0.998    -.0308303    .0309124
        F2event_b |   .0090266   .0144485     0.62   0.532    -.0193559    .0374091
        L0event_b |   .0073468    .023704     0.31   0.757    -.0392171    .0539107
        L1event_b |   .0179422   .0158282     1.13   0.257    -.0131506     .049035
        L2event_b |   .0355036   .0342427     1.04   0.300    -.0317625    .1027696
        L3event_b |   .0218985   .0276646     0.79   0.429    -.0324455    .0762426
        L4event_b |   .0188258   .0172688     1.09   0.276    -.0150968    .0527485
        L5event_b |  -.0164758   .0363637    -0.45   0.651    -.0879083    .0549566
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |  -.0102724   .0191101    -0.54   0.591    -.0478122    .0272674
        F6event_c |  -.0154774   .0130802    -1.18   0.237     -.041172    .0102172
        F5event_c |  -.0015734    .012434    -0.13   0.899    -.0259987    .0228519
        F4event_c |  -.0018438   .0085944    -0.21   0.830    -.0187266    .0150389
        F3event_c |  -.0074543   .0111006    -0.67   0.502    -.0292603    .0143516
        F2event_c |   .0080926   .0086068     0.94   0.348    -.0088146    .0249998
        L0event_c |   .4808823    .026789    17.95   0.000     .4282582    .5335065
        L1event_c |   .0010765   .0116017     0.09   0.926    -.0217137    .0238668
        L2event_c |  -.0173483   .0188845    -0.92   0.359    -.0544448    .0197482
        L3event_c |  -.1708644   .0511255    -3.34   0.001    -.2712949   -.0704338
        L4event_c |  -.0282224   .0416126    -0.68   0.498    -.1099658     .053521
        L5event_c |  -.0456886   .0348366    -1.31   0.190    -.1141213    .0227441
        L6event_c |   -.041149    .024322    -1.69   0.091     -.088927     .006629
        L7event_c |  -.0440139   .0459245    -0.96   0.338    -.1342276    .0461998
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -.1353889   .0513845    -2.63   0.009    -.2363283   -.0344496
         ew_biodt |  -.0039104    .001588    -2.46   0.014      -.00703   -.0007909
        ew_dtmihi |    -.00691   .0025372    -2.72   0.007     -.011894    -.001926
         ew_ledig |   .0008328   .0026606     0.31   0.754    -.0043936    .0060592
       ew_married |  -.0001281   .0027482    -0.05   0.963    -.0055267    .0052705
        wb_anteil |   .0020754   .0014026     1.48   0.140    -.0006799    .0048307
          wb_ausl |  -.0006932   .0005854    -1.18   0.237    -.0018431    .0004568
         wb_18t24 |   .0007631   .0012768     0.60   0.550     -.001745    .0032711
         wb_25t34 |   .0004787   .0008533     0.56   0.575    -.0011976    .0021549
         wb_35t44 |  -.0001558   .0011211    -0.14   0.890    -.0023581    .0020466
         wb_45t59 |   .0006119   .0009998     0.61   0.541    -.0013521    .0025758
          avg_dur |  -.0007049   .0010272    -0.69   0.493    -.0027227    .0013129
          hh_kids |   .0052928   .0020215     2.62   0.009     .0013218    .0092638
mpreis_flats_rent |   .0001763   .0013825     0.13   0.899    -.0025395    .0028921
            _cons |   1.116846   .4569406     2.44   0.015     .2192354    2.014456
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       538         538           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity
note: F1event_c omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,106
Absorbing 2 HDFE groups                           F(  54,    537) =     136.50
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7716
                                                  Adj R-squared   =     0.7172
                                                  Within R-sq.    =     0.5903
Number of clusters (sb_new)  =        538         Root MSE        =     0.1398

                                    (Std. err. adjusted for 538 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     treat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.0692031   .0455914    -1.52   0.130    -.1587625    .0203562
        F6event_a |  -.1221576   .0543363    -2.25   0.025    -.2288954   -.0154198
        F5event_a |  -.1070753   .0495525    -2.16   0.031    -.2044157   -.0097348
        F4event_a |   .0005587    .049301     0.01   0.991    -.0962877    .0974051
        F3event_a |   .0002289   .0436405     0.01   0.996    -.0854982    .0859559
        F2event_a |   .0086724    .040998     0.21   0.833    -.0718638    .0892086
        L0event_a |   .8145077   .0253948    32.07   0.000     .7646224    .8643929
        L1event_a |  -.0184809    .023589    -0.78   0.434     -.064819    .0278572
        L2event_a |   -.012985   .0247049    -0.53   0.599     -.061515     .035545
        L3event_a |   .0635094   .0583448     1.09   0.277    -.0511026    .1781213
        L4event_a |  -.0446634   .0453497    -0.98   0.325     -.133748    .0444211
        L5event_a |  -.1070473   .0544047    -1.97   0.050    -.2139194   -.0001752
        L6event_a |   .0034051   .0320128     0.11   0.915    -.0594806    .0662909
        L7event_a |  -.1033578   .1153049    -0.90   0.370    -.3298617    .1231462
        F7event_b |  -.0515423   .0294997    -1.75   0.081    -.1094913    .0064066
        F6event_b |  -.0292381   .0204981    -1.43   0.154    -.0695044    .0110283
        F5event_b |  -.0169753   .0342255    -0.50   0.620    -.0842077    .0502571
        F4event_b |  -.0393732   .0205649    -1.91   0.056    -.0797708    .0010244
        F3event_b |  -.0444458   .0199221    -2.23   0.026    -.0835806    -.005311
        F2event_b |  -.0371543   .0157491    -2.36   0.019    -.0680917    -.006217
        L0event_b |   .7946611    .019817    40.10   0.000     .7557328    .8335895
        L1event_b |  -.0201956   .0230554    -0.88   0.381    -.0654855    .0250943
        L2event_b |   .0131224   .0378857     0.35   0.729       -.0613    .0875448
        L3event_b |   .1053564   .0629121     1.67   0.095    -.0182277    .2289404
        L4event_b |   .0010399   .0258951     0.04   0.968    -.0498282    .0519079
        L5event_b |   -.084283   .0837622    -1.01   0.315    -.2488247    .0802587
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F7event_c |  -.0014891   .0449972    -0.03   0.974    -.0898811     .086903
        F6event_c |  -.0350403   .0204022    -1.72   0.086    -.0751183    .0050377
        F5event_c |  -.0727322   .0289812    -2.51   0.012    -.1296626   -.0158018
        F4event_c |  -.0184515   .0218019    -0.85   0.398     -.061279    .0243761
        F3event_c |  -.0301292   .0192096    -1.57   0.117    -.0678644     .007606
        F2event_c |  -.0263288   .0175511    -1.50   0.134    -.0608061    .0081485
        L0event_c |   .8361963   .0213834    39.10   0.000     .7941909    .8782016
        L1event_c |  -.0192353   .0219898    -0.87   0.382    -.0624318    .0239612
        L2event_c |   .0133386   .0271408     0.49   0.623    -.0399765    .0666537
        L3event_c |   .0503514   .0721917     0.70   0.486    -.0914614    .1921643
        L4event_c |  -.0756511   .0564309    -1.34   0.181    -.1865034    .0352013
        L5event_c |    .006175   .0811608     0.08   0.939    -.1532566    .1656065
        L6event_c |    .018568   .0362329     0.51   0.609    -.0526077    .0897437
        L7event_c |  -.1403653   .0743204    -1.89   0.059    -.2863597    .0056291
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        F1event_c |          0  (omitted)
        ln_ew_ges |  -.0434594   .0992943    -0.44   0.662    -.2385122    .1515934
         ew_biodt |  -.0007755    .002651    -0.29   0.770    -.0059832    .0044322
        ew_dtmihi |  -.0065801   .0039979    -1.65   0.100    -.0144336    .0012733
         ew_ledig |   .0054135   .0047439     1.14   0.254    -.0039054    .0147324
       ew_married |   .0026081   .0046102     0.57   0.572    -.0064481    .0116643
        wb_anteil |  -.0002046   .0020555    -0.10   0.921    -.0042424    .0038332
          wb_ausl |  -.0014963   .0008628    -1.73   0.083    -.0031912    .0001986
         wb_18t24 |  -.0024965   .0020591    -1.21   0.226    -.0065415    .0015485
         wb_25t34 |   .0001584   .0011728     0.14   0.893    -.0021455    .0024623
         wb_35t44 |   .0008823   .0017442     0.51   0.613    -.0025441    .0043086
         wb_45t59 |   .0004808   .0015458     0.31   0.756    -.0025556    .0035173
          avg_dur |    .001269   .0014125     0.90   0.369    -.0015056    .0040437
          hh_kids |   .0042263    .003201     1.32   0.187    -.0020617    .0105143
mpreis_flats_rent |   -.002048   .0021379    -0.96   0.339    -.0062477    .0021518
            _cons |   .1288958   .9057475     0.14   0.887    -1.650347    1.908139
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       538         538           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         * PLOT: FIGURE D9. Effect Heterogeneity by Change in Proximity Restricted to Cases with 
> Consistent Distance Changes     
.         grc1leg del_street_dist turnout_urne turnout_pos_req turnout_tot_req, col(2)  xcommon po
> s(6) imargins(small)

.         gr_edit .plotregion1.graph1.yaxis1.reset_rule -0.6 0.6 .3 , tickset(major) ruletype(rang
> e) 

.         gr_edit .plotregion1.graph1.yaxis1.title.text = {}

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"Change in distance in km"'

.         gr_edit .plotregion1.graph1.yaxis1.title.text.Arrpush `"(estimates)"'

.         gr_edit .style.editstyle declared_ysize(4) editcopy

.         graph export "$figures/Figure_D9_ES_het_by_distance_ambi.pdf", replace  
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D9_ES_
    > het_by_distance_ambi.pdf saved as PDF format

. 
end of do-file
Running: 04c_rob_party_district_level_figure_d15.do

. /*
> 
> Input: newdata/estimation_prep_ltw18_stadtbez.dta [prepared district-level panel]
> 
> Output: Figure D.15
> 
> Task: Robustness: Effect on party outcomes by mail, district level 
> 
> */      
.         
. * PULL: District-level panel
.         use "$newdata/estimation_prep_ltw18_stadtbez.dta", clear

.                 
. ********************************************************************************
.         //       Prep Estimation //
. ********************************************************************************
.         
.         // gen total turnout
.         gen sbez_turnout_tot_req = sbez_turnout_urne_ohne + sbez_turnout_pos_req

.         
.         drop *heitsforschung *rrp

.         
. // insert missings instead of zeros for party shares where party wasn't on the ballot
.         foreach v of varlist sbez_shr_* {
  2.                 forvalues j =1/8 {
  3.                         qui su `v' if wahl_id==`j'
  4.                         qui replace `v' =. if r(mean)==0 & wahl_id==`j'
  5.                 }
  6.                 assert `v' <1 if !missing(`v')
  7.                 qui replace `v'= `v'*100 // rescale 0-100
  8.         }       

.         
.         // gen votes rel to eligible voter
.         * in KOW14 und KOW20 => #votes > #eligible voters!
.         foreach v of varlist sbez_anz_* {
  2.                 gen      rel_`v'  =  100* `v' /wahlber_gesamt if !inlist(wahl_id,3,8)
  3.                 replace  rel_`v' =  100* `v' /(wahlber_gesamt*80) if inlist(wahl_id,3,8)
  4.                 assert inrange(rel_`v',0,100) | missing(rel_`v')
  5.         }               
(50 missing values generated)
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. 
.         // GEN party shares by MAIL
.         * in KOW14 und KOW20 => #votes > #eligible voters!
.         foreach v of varlist sbez_anz_p* {
  2.                 gen      shr_`v'  =  100* `v' /(waehler_21) if !inlist(wahl_id,3,8)
  3.                 replace  shr_`v' =  100* `v' /((waehler_21)*80) if inlist(wahl_id,3,8)
  4.         }               
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.         
.         // classify 6 largest parties in Left-Right (ONLY POSTAL)
.         gen shr_sbez_anz_pleft  = shr_sbez_anz_pdielinke + shr_sbez_anz_pspd + shr_sbez_anz_pgru
> ene

.         gen shr_sbez_anz_pcons  = shr_sbez_anz_pfdp + shr_sbez_anz_pcsu  + shr_sbez_anz_pfreiewa
> ehler 

.         gen rel_sbez_anz_pleft  = rel_sbez_anz_pdielinke + rel_sbez_anz_pspd + rel_sbez_anz_pgru
> ene

.         gen rel_sbez_anz_pcons  = rel_sbez_anz_pfdp + rel_sbez_anz_pcsu  + rel_sbez_anz_pfreiewa
> ehler 

.         
.         ** Define Event= first time precinct is >70% reassigned
.         * Gen treat50_simple = Binary indicator for treat_simple 
.         gen treat50_simple = (treat_simple >=.7) 

.         
.         * Gen  fulltotreat50= Total nbr. of times a precinct is treated (>70% reassigned)
.         bys stadtbez: egen fulltottreat50 = total(treat50_simple)       

.         
.         
. * Gen relevant indicators       
.         // gen Ei50 = unit-specific date of treatment (never-treated = missing)
.         cap drop Ei50

.         gen tmp_wahl_id = (-1)*wahl_id

.         bys stadtbez (treat50_simple tmp_wahl_id): gen Ei50 = wahl_id[_N] if fulltottreat50>0 //
>  wahl_id where first FULL-treatment (treat_simple==1)
(176 missing values generated)

.         lab var Ei50 "date of treatment,treat=70% reassigned, NT=."

.         
.         // gen K := "relative time", i.e. the number periods since treated (never-treated = miss
> ing)
.         cap drop K50

.         gen K50 = wahl_id- Ei50 
(176 missing values generated)

.         lab var K50 "rel. time, treat=70% reassigned, NT=."

.         
.         // gen D treatment indicator (=1 post treatmeant, never-treated = 0)
.         cap drop D50

.         gen D50 = K50>=0 & Ei50!=. 

.         lab var D50 "post-treatment dummy, treat=70% reassigned, NT=0"

.         
.         // smpl_trim50: drop observations after SECOND treatment (>50%) kicks in
.         cap drop        smpl_trim50

.         gen             smpl_trim50 = 1

.         gen tmp_other_treats = (treat50_simple > 0 & K50 != 0)          // identify second treat
> ments after t0

.         bys stadtbez (wahl_id): replace smpl_trim50 = 0 if (tmp_other_treats == 1 | smpl_trim50[
> _n-1] ==0) // set missing after second treatment kicks in               
(3 real changes made)

.         
.         tab stadtbez if K50==0

District ID |
(Stadtbezir |
         k) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          1       33.33       33.33
          5 |          1       33.33       66.67
         25 |          1       33.33      100.00
------------+-----------------------------------
      Total |          3      100.00

.         count  if K50==0                        // how many out of 25 treated
  3

.         
. ********************************************************************************
.         // Event Study: Party Outcomes, DISTRICT level, Figure D15 //
. ********************************************************************************
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K50==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K50==`l'
  3.         }

.         order F1event, last

. 
.         // Estimate baseline ES
.         estimates clear

.         global dpv rel_sbez_anz_pleft shr_sbez_anz_pleft rel_sbez_anz_pcons  shr_sbez_anz_pcons

.         
.         
.         foreach v of varlist $dpv {     
  2.                 qui reghdfe `v' F7event-L7event F1event $ctr $wgt if smpl_trim50 ==1, absorb(
> i.wahl_id i.stadtbez) cluster(stadtbez)
  3.                 estimates store `v'
  4.                 
.                 qui su `v' $wgt
  5.                 local mean_`v':di %12.1f r(mean)
  6.                 local m`v'=subinstr("`mean_`v''"," ","",.)
  7.         }

.                 
.                 
.         // PLOT: LEFT RIGHT PARTY TURNOUT
.         event_plot  rel_sbez_anz_pleft  rel_sbez_anz_pcons , ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Party turnout in %""(estimates)", size(medsmall)) xlabel(-4
> (1)2) xtitle("Election since reassignment") ///
>                 legend(pos(12) order(1 "Left-wing (`mrel_sbez_anz_pleft' %)" 3 "Right-wing (`mre
> l_sbez_anz_pcons' %)") row(1) region(style(none)) size(medsmall) title("Outcomes (means):",size(
> medsmall) bexpand just(left))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 name(turnout, replace)) ///
>         lag_opt1(msymbol(O) msize(2.5pt) color(maroon))         lag_ci_opt1(color(maroon)) ///
>         lag_opt2(msymbol(S) msize(2.5pt) color(black)) lag_ci_opt2(color(black))        

.         
.         // PLOT: LEFT RIGHT PARTY VOTE SHARES
.         event_plot  shr_sbez_anz_pleft shr_sbez_anz_pcons , ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Party vote share in %""(estimates)", size(medsmall)) xlabel
> (-4(1)2) xtitle("Election since reassignment") ///
>                 legend(pos(12) order(1 "Left-wing (`mshr_sbez_anz_pleft' %)" 3  "Right-wing (`ms
> hr_sbez_anz_pcons' %)"   ) row(1) region(style(none)) size(medsmall) title("Outcomes (means):",s
> ize(medsmall) bexpand just(left))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 name(shares, replace) ) ///
>         lag_opt1(msymbol(O) msize(2.5pt) color(maroon))  lag_ci_opt1(color(maroon)) ///
>         lag_opt2(msymbol(S) msize(2.5pt) color(black)) lag_ci_opt2(color(black))        

. 
.         
. * Test for equality (note: since different outcomes => need to duplicate data)
. frame change default

. frame copy default tmp, replace

. frame tmp{      
.         // duplicate dataset
.         gen id=_n
.         expand 2
(200 observations created)
.         
.         // gen dataset id
.         bys id: gen idset = _n-1
.         
.         // rename outcomes
.         gen     y_shr = shr_sbez_anz_pleft       if idset==1
(200 missing values generated)
.         replace y_shr = shr_sbez_anz_pcons       if idset==0
(200 real changes made)
.         gen     y_rel = rel_sbez_anz_pleft if idset==1
(200 missing values generated)
.         replace y_rel = rel_sbez_anz_pcons if idset==0  
(200 real changes made)
.         
. // gen leads and lags
.         cap drop L* F*
.         forvalues l = 7(-1)1 {
  2.                 gen     F`l'event = K50==-`l'
  3.                 gen     F`l'event_int =F`l'event*idset  // interact w/ dataset id
  4.         }       
.         forvalues l = 0/7 {
  2.                 gen     L`l'event = K50==`l'
  3.                 gen     L`l'event_int =L`l'event*idset
  4.         }
.         order *_int, last
.         order F1event*, last
. 
.         // Estimate baseline ES
.         estimates clear
. 
.         global dpv y_rel y_shr 
.         
.         foreach v of varlist $dpv {     
  2.                   reghdfe `v' F7event-L7event F7event_int-L7event_int F1event F1event_int c.(
> $ctr)##c.idset $wgt if smpl_trim50 ==1, ///
>                         absorb(i.wahl_id##i.idset i.stadtbez##i.idset) cluster(stadtbez)
  3.                 
.                 estimates store `v'
  4.                                         
.                 // PLOT differences
.                 event_plot  `v', ///
>                 stub_lag(L#event_int ) stub_lead(F#event_int ) plottype(connect) ciplottype(rcap
> ) ///
>                 together  trimlead(4) trimlag(2)  noautolegend ///
>                 graph_opt(ytitle("Difference between estimates", size(medsmall)) xlabel(-4(1)2) 
> xtitle("Election since reassignment", size(medsmall)) ///
>                         legend(pos(12) order(1 "Difference between estimates" ) row(1) region(st
> yle(none)) size(medsmall) title(" ")) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         name(`v',replace) ) ///
>                 lag_opt1(msymbol(T) msize(2.5pt) color(black) lcol(gray))       lag_ci_opt1(colo
> r(black))               
  5.         }
(MWFE estimator converged in 4 iterations)
note: idset is probably collinear with the fixed effects (all partialled-out values are close to z
> ero; tol = 1.0e-09)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: F5event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F7event_int omitted because of collinearity
note: F6event_int omitted because of collinearity
note: F5event_int omitted because of collinearity
note: L4event_int omitted because of collinearity
note: L5event_int omitted because of collinearity
note: L6event_int omitted because of collinearity
note: L7event_int omitted because of collinearity
note: F1event omitted because of collinearity
note: F1event_int omitted because of collinearity
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        394
Absorbing 2 HDFE groups                           F(  42,     24) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.9721
                                                  Adj R-squared   =     0.9618
                                                  Within R-sq.    =     0.3342
Number of clusters (stadtbez) =         25        Root MSE        =     0.6744

                                             (Std. err. adjusted for 25 clusters in stadtbez)
---------------------------------------------------------------------------------------------
                            |               Robust
                      y_rel | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
                    F7event |          0  (omitted)
                    F6event |          0  (omitted)
                    F5event |          0  (omitted)
                    F4event |   -.247598   .3127638    -0.79   0.436    -.8931107    .3979147
                    F3event |   .2226054   .5558476     0.40   0.692    -.9246076    1.369818
                    F2event |  -.2958135   .4388617    -0.67   0.507    -1.201579    .6099525
                    L0event |   .4690402   .8854708     0.53   0.601    -1.358482    2.296562
                    L1event |   -.037418   .5448232    -0.07   0.946    -1.161878    1.087042
                    L2event |    .010947   .2817753     0.04   0.969    -.5706086    .5925027
                    L3event |  -.3491429   .4075465    -0.86   0.400    -1.190277    .4919916
                    L4event |          0  (omitted)
                    L5event |          0  (omitted)
                    L6event |          0  (omitted)
                    L7event |          0  (omitted)
                F7event_int |          0  (omitted)
                F6event_int |          0  (omitted)
                F5event_int |          0  (omitted)
                F4event_int |   .5463512   .2946066     1.85   0.076    -.0616869    1.154389
                F3event_int |   .6109057   .4550272     1.34   0.192    -.3282243    1.550036
                F2event_int |   .0380136   .5424636     0.07   0.945    -1.081576    1.157604
                L0event_int |   .0629842   1.002894     0.06   0.950    -2.006887    2.132855
                L1event_int |   .3030169   .2954598     1.03   0.315    -.3067822     .912816
                L2event_int |   .7453796   .3734472     2.00   0.057    -.0253776    1.516137
                L3event_int |   1.327633   .4912189     2.70   0.012     .3138067    2.341459
                L4event_int |          0  (omitted)
                L5event_int |          0  (omitted)
                L6event_int |          0  (omitted)
                L7event_int |          0  (omitted)
                    F1event |          0  (omitted)
                F1event_int |          0  (omitted)
                  ln_ew_ges |  -7.548475   6.913505    -1.09   0.286    -21.81725    6.720299
                   ew_biodt |  -.4862748    .141462    -3.44   0.002     -.778238   -.1943116
                  ew_dtmihi |  -.0094656   .2842838    -0.03   0.974    -.5961985    .5772674
                   ew_ledig |   -.343043   .2583188    -1.33   0.197    -.8761867    .1901008
                 ew_married |  -.1778457   .4833007    -0.37   0.716    -1.175329    .8196379
                  wb_anteil |    .034547   .0768477     0.45   0.657    -.1240588    .1931528
                    wb_ausl |  -1.40e-07   8.30e-07    -0.17   0.867    -1.85e-06    1.57e-06
                   wb_18t24 |   .0004803   .0005317     0.90   0.375    -.0006171    .0015777
                   wb_25t34 |  -.0005125   .0002529    -2.03   0.054    -.0010345    9.57e-06
                   wb_35t44 |   .0006043   .0002835     2.13   0.043     .0000191    .0011894
                   wb_45t59 |  -.0001673    .000238    -0.70   0.489    -.0006584    .0003239
                    avg_dur |   .2808103   .3147336     0.89   0.381     -.368768    .9303885
                    hh_kids |  -.0320482   .1586042    -0.20   0.842    -.3593912    .2952947
          mpreis_flats_rent |   .0741307   .0755607     0.98   0.336    -.0818188    .2300803
                      idset |          0  (omitted)
                            |
        c.ln_ew_ges#c.idset |   2.408503   8.867496     0.27   0.788    -15.89311    20.71011
                            |
         c.ew_biodt#c.idset |   .8421011   .1839744     4.58   0.000     .4623967    1.221805
                            |
        c.ew_dtmihi#c.idset |  -.2838422   .3123456    -0.91   0.373    -.9284919    .3608075
                            |
         c.ew_ledig#c.idset |   .2830568   .3542893     0.80   0.432    -.4481603    1.014274
                            |
       c.ew_married#c.idset |   .7074858   .6363118     1.11   0.277    -.6057973    2.020769
                            |
        c.wb_anteil#c.idset |  -.3189792   .0953389    -3.35   0.003     -.515749   -.1222094
                            |
          c.wb_ausl#c.idset |  -1.04e-07   4.99e-07    -0.21   0.836    -1.13e-06    9.25e-07
                            |
         c.wb_18t24#c.idset |   .0007573   .0006208     1.22   0.234    -.0005239    .0020386
                            |
         c.wb_25t34#c.idset |  -.0000968   .0003228    -0.30   0.767    -.0007631    .0005694
                            |
         c.wb_35t44#c.idset |  -.0001308   .0003141    -0.42   0.681     -.000779    .0005174
                            |
         c.wb_45t59#c.idset |    .000206    .000234     0.88   0.387     -.000277    .0006889
                            |
          c.avg_dur#c.idset |  -.0199361   .4434323    -0.04   0.965    -.9351354    .8952631
                            |
          c.hh_kids#c.idset |   .2027026   .1898261     1.07   0.296    -.1890791    .5944843
                            |
c.mpreis_flats_rent#c.idset |   -.047701   .0476449    -1.00   0.327    -.1460352    .0506332
                            |
                      _cons |   89.93516   77.15485     1.17   0.255    -69.30461    249.1749
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
----------------------------------------------------------+
      Absorbed FE | Categories  - Redundant  = Num. Coefs |
------------------+---------------------------------------|
    wahl_id#idset |        16           1          15     |
   stadtbez#idset |        50          50           0    *|
----------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 4 iterations)
note: idset is probably collinear with the fixed effects (all partialled-out values are close to z
> ero; tol = 1.0e-09)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: F5event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F7event_int omitted because of collinearity
note: F6event_int omitted because of collinearity
note: F5event_int omitted because of collinearity
note: L4event_int omitted because of collinearity
note: L5event_int omitted because of collinearity
note: L6event_int omitted because of collinearity
note: L7event_int omitted because of collinearity
note: F1event omitted because of collinearity
note: F1event_int omitted because of collinearity
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        394
Absorbing 2 HDFE groups                           F(  42,     24) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.9798
                                                  Adj R-squared   =     0.9723
                                                  Within R-sq.    =     0.2418
Number of clusters (stadtbez) =         25        Root MSE        =     1.2285

                                             (Std. err. adjusted for 25 clusters in stadtbez)
---------------------------------------------------------------------------------------------
                            |               Robust
                      y_shr | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
                    F7event |          0  (omitted)
                    F6event |          0  (omitted)
                    F5event |          0  (omitted)
                    F4event |  -1.187029   .5941645    -2.00   0.057    -2.413324    .0392661
                    F3event |  -.0659515   .8649001    -0.08   0.940    -1.851018    1.719115
                    F2event |  -1.436758   .8327004    -1.73   0.097    -3.155367    .2818515
                    L0event |   .8044601   1.338068     0.60   0.553    -1.957176    3.566096
                    L1event |   .1332099   .9780731     0.14   0.893    -1.885434    2.151854
                    L2event |   -.727146   1.586303    -0.46   0.651    -4.001115    2.546823
                    L3event |  -.9688179   .9125088    -1.06   0.299    -2.852143    .9145077
                    L4event |          0  (omitted)
                    L5event |          0  (omitted)
                    L6event |          0  (omitted)
                    L7event |          0  (omitted)
                F7event_int |          0  (omitted)
                F6event_int |          0  (omitted)
                F5event_int |          0  (omitted)
                F4event_int |   1.150493   .7906135     1.46   0.159    -.4812527    2.782239
                F3event_int |   .2063724    1.78052     0.12   0.909     -3.46844    3.881185
                F2event_int |    .599523   1.394131     0.43   0.671    -2.277823    3.476869
                L0event_int |  -1.970731   2.624431    -0.75   0.460    -7.387291    3.445829
                L1event_int |  -.3028238   1.664717    -0.18   0.857     -3.73863    3.132983
                L2event_int |   1.053571   2.671207     0.39   0.697     -4.45953    6.566671
                L3event_int |   2.651719   1.485006     1.79   0.087    -.4131818     5.71662
                L4event_int |          0  (omitted)
                L5event_int |          0  (omitted)
                L6event_int |          0  (omitted)
                L7event_int |          0  (omitted)
                    F1event |          0  (omitted)
                F1event_int |          0  (omitted)
                  ln_ew_ges |   2.714371   14.28323     0.19   0.851    -26.76476     32.1935
                   ew_biodt |  -.1301144   .3793923    -0.34   0.735    -.9131415    .6529128
                  ew_dtmihi |   1.118594   .5863979     1.91   0.068    -.0916714     2.32886
                   ew_ledig |  -.2136189   .4530589    -0.47   0.642    -1.148686    .7214487
                 ew_married |  -.5201656   .7451598    -0.70   0.492      -2.0581    1.017769
                  wb_anteil |  -.2622409   .1254822    -2.09   0.047    -.5212234   -.0032583
                    wb_ausl |  -1.39e-07   8.71e-07    -0.16   0.875    -1.94e-06    1.66e-06
                   wb_18t24 |  -.0001635   .0007216    -0.23   0.823    -.0016527    .0013258
                   wb_25t34 |  -.0003563    .000375    -0.95   0.352    -.0011303    .0004177
                   wb_35t44 |   .0004762   .0004331     1.10   0.282    -.0004177    .0013701
                   wb_45t59 |  -.0002642   .0003269    -0.81   0.427    -.0009389    .0004104
                    avg_dur |    .082418   .5897056     0.14   0.890    -1.134674     1.29951
                    hh_kids |  -.3687036    .325474    -1.13   0.268    -1.040449    .3030418
          mpreis_flats_rent |   .1669331   .1356697     1.23   0.230    -.1130755    .4469416
                      idset |          0  (omitted)
                            |
        c.ln_ew_ges#c.idset |  -15.50743   31.08881    -0.50   0.622    -79.67157    48.65671
                            |
         c.ew_biodt#c.idset |    .278439   .6170864     0.45   0.656    -.9951648    1.552043
                            |
        c.ew_dtmihi#c.idset |  -1.846154     1.1137    -1.66   0.110    -4.144718    .4524095
                            |
         c.ew_ledig#c.idset |   .7080516   .8545004     0.83   0.415    -1.055551    2.471654
                            |
       c.ew_married#c.idset |   1.990509   1.730572     1.15   0.261    -1.581217    5.562234
                            |
        c.wb_anteil#c.idset |   .1017041   .1772259     0.57   0.571    -.2640721    .4674803
                            |
          c.wb_ausl#c.idset |   2.64e-07   1.24e-06     0.21   0.833    -2.29e-06    2.82e-06
                            |
         c.wb_18t24#c.idset |   .0020631   .0013258     1.56   0.133    -.0006732    .0047994
                            |
         c.wb_25t34#c.idset |  -.0000788   .0007445    -0.11   0.917    -.0016153    .0014578
                            |
         c.wb_35t44#c.idset |  -.0004174   .0008859    -0.47   0.642    -.0022459     .001411
                            |
         c.wb_45t59#c.idset |   .0006853   .0005597     1.22   0.233    -.0004698    .0018404
                            |
          c.avg_dur#c.idset |   .6212781   1.220885     0.51   0.615    -1.898504     3.14106
                            |
          c.hh_kids#c.idset |   .8675406   .6152199     1.41   0.171    -.4022109    2.137292
                            |
c.mpreis_flats_rent#c.idset |  -.2694293   .2334316    -1.15   0.260    -.7512085    .2123499
                            |
                      _cons |   71.93973   114.9085     0.63   0.537    -165.2197    309.0991
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
----------------------------------------------------------+
      Absorbed FE | Categories  - Redundant  = Num. Coefs |
------------------+---------------------------------------|
    wahl_id#idset |        16           1          15     |
   stadtbez#idset |        50          50           0    *|
----------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
. }       

.         
.         * PLOT: FIGURE D15. Effects of Reassignments on Party Outcomes by Mail, district level
.         graph combine turnout y_rel, row(1)             imargins(small) xcommon ycommon name(gr1
> , replace) ///
>                 title("{bf:Panel A.} Effect on Party Turnout", nobox span bexpand justification(
> left) size(medsmall) )

.         graph combine shares y_shr, row(1) imargins(small) xcommon ycommon name(gr2, replace) //
> /
>                 title("{bf:Panel B.} Effect on Party Vote Shares", nobox span bexpand justificat
> ion(left) size(medsmall) )

.         
.         graph combine gr1 gr2, xcommon col(1) imargins(small) iscale(.9)        

.         gr_edit .style.editstyle declared_ysize(4.2) editcopy   

.         graph export "$figures/Figure_D15_ES_party_outc_two_district_postal.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D15_ES
    > _party_outc_two_district_postal.pdf saved as PDF format

. 
end of do-file
Running: 04d_rob_het_by_reason_figure_c2.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
>         
> Output: Figure C.2
> 
> Tasks: Heterogeneity by reason of reassignment
> 
>         
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
. ********************************************************************************
.                 // Prep Estimation //
. ********************************************************************************                
>         
.         // compute group ids (ind_treat_consol ind_treat_no_consol) 
.         cap drop tmp*

.         gen     tmp = (treat_consol>0& treat_consol>treat_no_consol)    if K50==0
(4,572 missing values generated)

.         bys sb_new (tmp): gen ind_treat_consol = tmp[1]
(1,968 missing values generated)

.         replace ind_treat_consol = 0                                                            
>         if missing(Ei50)
(1,968 real changes made)

.         lab var ind_treat_consol "=1 if event due to reconfiguration, 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (treat_no_consol>0& treat_consol<treat_no_consol) if K50==0
(4,572 missing values generated)

.         bys sb_new (tmp): gen ind_treat_no_consol = tmp[1]
(1,968 missing values generated)

.         replace ind_treat_no_consol = 0                                                         
>         if missing(Ei50)        
(1,968 real changes made)

.         lab var ind_treat_no_consol "=1 if event due to recruitment, 0 else"

.                 
. 
. ********************************************************************************
.         // Heterogeneity by reason of reassignment (Figure C2) //
. ********************************************************************************                
.         
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K50==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K50==`l'
  3.         }

.         
.         // Create two set of dummies: Reason Dummy x rel. time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_treat_no_consol    // a := Recruitment
  3.                 gen             F`l'event_b = F`l'event *ind_treat_consol               // b:
> = Reconfig
  4.                 assert  F`l'event_b+F`l'event_a==F`l'event              
  5.                 
.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_treat_no_consol    // a := Recruitment
  3.                 gen             L`l'event_b = L`l'event *ind_treat_consol               // b:
> = Reconfig
  4.                 assert  L`l'event_b+L`l'event_a==L`l'event              
  5.                 
.         }               

.         
.         // ORDER dummies
.         order *event_b, last

.         order F1event*,last     

.         
.         
.         // Estimate ES by reason of reassignment
.         estimates clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req del_street_dist treat_
> simple{
  2.         
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b ///
>                                 $ctr $wgt if smpl_trim50 ==1, absorb(i.wahl_id#i.stadtbez i.sb_n
> ew) cluster(sb_new)             
  3.                 
.                 estimates store `v'_a
  4.                 estimates store `v'_b   
  5. 
.                 
.                 // PLOT Outcome + save
.                 event_plot  `v'_a `v'_b , ///
>                 stub_lag(L#event_a L#event_b ) stub_lead(F#event_a F#event_b ) plottype(connect)
>  ciplottype(rcap) ///
>                 together perturb(-0.1(0.2)0.1) trimlead(4) trimlag(2) noautolegend ///
>                 graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(small)) xlab
> el(-4(1)2) xtitle("Election since reassignment") ///
>                         legend(pos(12) order(1 "Polling location recruitment" 3 "Precinct reconf
> iguration" ) row(1) region(style(none)) bexpand just(left) span) ///
>                         xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(so
> lid)) ylabel(, angle(horizontal)) ///
>                         name(`v', replace)) ///
>                 lag_opt1(msymbol(S) msize(2.5pt) color(black))  lag_ci_opt1(color(black)) ///
>                 lag_opt2(msymbol(O) msize(2.5pt) color(maroon)) lag_ci_opt2(color(maroon))      
>                         
  6.         }
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  42,    617) =      13.28
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9728
                                                  Adj R-squared   =     0.9664
                                                  Within R-sq.    =     0.1903
Number of clusters (sb_new)  =        618         Root MSE        =     1.6729

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.3069995   .3581544    -0.86   0.392    -1.010349      .39635
        F6event_a |  -.2759617   .3331098    -0.83   0.408     -.930128    .3782047
        F5event_a |  -.0214501   .2666074    -0.08   0.936    -.5450181    .5021179
        F4event_a |  -.0497066   .1716005    -0.29   0.772    -.3866984    .2872852
        F3event_a |  -.1930033   .1778814    -1.09   0.278    -.5423297    .1563232
        F2event_a |  -.1667295   .1219367    -1.37   0.172    -.4061907    .0727317
        L0event_a |    -.97476    .250315    -3.89   0.000    -1.466333   -.4831873
        L1event_a |  -1.063097   .2603819    -4.08   0.000     -1.57444   -.5517551
        L2event_a |  -.9492275   .2663704    -3.56   0.000     -1.47233    -.426125
        L3event_a |  -.7680981   .2819093    -2.72   0.007    -1.321716     -.21448
        L4event_a |  -.6667608   .5554222    -1.20   0.230    -1.757508    .4239864
        L5event_a |  -.9154047   .5061351    -1.81   0.071    -1.909361    .0785517
        L6event_a |  -.3007365   .8507653    -0.35   0.724    -1.971483     1.37001
        L7event_a |  -.7167549   .8292706    -0.86   0.388     -2.34529    .9117802
        F7event_b |  -.8761352   .4444513    -1.97   0.049    -1.748956   -.0033145
        F6event_b |  -.6329785   .4104291    -1.54   0.124    -1.438986    .1730287
        F5event_b |  -.3030915   .3706172    -0.82   0.414    -1.030916    .4247326
        F4event_b |  -.4224059   .2542282    -1.66   0.097    -.9216634    .0768516
        F3event_b |  -.3942096   .2547796    -1.55   0.122    -.8945499    .1061307
        F2event_b |   .1737288   .1980249     0.88   0.381    -.2151556    .5626133
        L0event_b |  -1.725251   .3084618    -5.59   0.000    -2.331014   -1.119489
        L1event_b |    -2.1214   .3412532    -6.22   0.000    -2.791559   -1.451242
        L2event_b |  -1.586727   .3961034    -4.01   0.000    -2.364601   -.8088527
        L3event_b |  -.3865574   .4123749    -0.94   0.349    -1.196386    .4232711
        L4event_b |   -.888709   1.035297    -0.86   0.391    -2.921843    1.144425
        L5event_b |  -1.217967   1.301896    -0.94   0.350    -3.774653    1.338718
        L6event_b |   -1.72762   .8565284    -2.02   0.044    -3.409684   -.0455557
        L7event_b |  -.6234767   .6636002    -0.94   0.348    -1.926666    .6797122
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -1.195237   .9636545    -1.24   0.215    -3.087677    .6972037
         ew_biodt |   .3614684   .0289849    12.47   0.000     .3045473    .4183895
        ew_dtmihi |   .0496537   .0534621     0.93   0.353    -.0553359    .1546434
         ew_ledig |   .1970908   .0554625     3.55   0.000     .0881727    .3060088
       ew_married |   .4203398   .0583558     7.20   0.000     .3057396    .5349399
        wb_anteil |   -.291025   .0210475   -13.83   0.000    -.3323583   -.2496917
          wb_ausl |    .015161   .0164197     0.92   0.356    -.0170842    .0474063
         wb_18t24 |  -.0261287   .0292548    -0.89   0.372    -.0835798    .0313224
         wb_25t34 |  -.0699852   .0197083    -3.55   0.000    -.1086886   -.0312818
         wb_35t44 |  -.0077827   .0226659    -0.34   0.731    -.0522945     .036729
         wb_45t59 |   .0074159   .0222324     0.33   0.739    -.0362444    .0510762
          avg_dur |  -.0240108   .0219454    -1.09   0.274    -.0671074    .0190859
          hh_kids |   -.032257   .0422712    -0.76   0.446    -.1152698    .0507558
mpreis_flats_rent |   .0236103   .0259026     0.91   0.362    -.0272576    .0744783
            _cons |    17.1813   9.411509     1.83   0.068    -1.301176    35.66377
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  42,    617) =      11.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9625
                                                  Adj R-squared   =     0.9538
                                                  Within R-sq.    =     0.2122
Number of clusters (sb_new)  =        618         Root MSE        =     1.6678

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .5389925   .3505126     1.54   0.125    -.1493499    1.227335
        F6event_a |   .6686601   .2846675     2.35   0.019     .1096254    1.227695
        F5event_a |  -.2713495   .3372084    -0.80   0.421    -.9335648    .3908659
        F4event_a |  -.1518441   .1742456    -0.87   0.384    -.4940304    .1903422
        F3event_a |   -.036926   .1526406    -0.24   0.809    -.3366842    .2628322
        F2event_a |   .0291814   .1218088     0.24   0.811    -.2100287    .2683915
        L0event_a |   .4578603   .2259444     2.03   0.043     .0141469    .9015736
        L1event_a |   .8127218   .2515795     3.23   0.001     .3186659    1.306778
        L2event_a |   1.206959   .2739842     4.41   0.000     .6689049    1.745014
        L3event_a |   .6464143   .2792514     2.31   0.021     .0980158    1.194813
        L4event_a |   1.006888   .6604889     1.52   0.128    -.2901907    2.303967
        L5event_a |   1.267113   .6036412     2.10   0.036     .0816727    2.452553
        L6event_a |   .0305716   .6614299     0.05   0.963    -1.268355    1.329498
        L7event_a |   .5660279   .7071181     0.80   0.424    -.8226221    1.954678
        F7event_b |   .5765791   .4002901     1.44   0.150    -.2095172    1.362675
        F6event_b |   .6063473   .3552087     1.71   0.088    -.0912174    1.303912
        F5event_b |    .380365   .3516055     1.08   0.280    -.3101237    1.070854
        F4event_b |   .1322542   .2069277     0.64   0.523    -.2741137    .5386221
        F3event_b |   .2245881   .2250356     1.00   0.319    -.2173404    .6665167
        F2event_b |  -.2697005   .1854471    -1.45   0.146    -.6338846    .0944836
        L0event_b |   1.113463   .3216602     3.46   0.001     .4817819    1.745145
        L1event_b |   1.759308   .3530228     4.98   0.000     1.066036    2.452579
        L2event_b |   1.558708   .3814637     4.09   0.000     .8095837    2.307833
        L3event_b |   1.095155   .4529623     2.42   0.016     .2056207     1.98469
        L4event_b |   2.318589   .9575305     2.42   0.016     .4381747    4.199003
        L5event_b |   2.739659   1.414252     1.94   0.053    -.0376729     5.51699
        L6event_b |   4.381078   1.230218     3.56   0.000     1.965155       6.797
        L7event_b |   2.169815   .8116469     2.67   0.008     .5758894     3.76374
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |    3.34693   1.332333     2.51   0.012     .7304724    5.963388
         ew_biodt |   .4016687   .0308029    13.04   0.000     .3411775      .46216
        ew_dtmihi |  -.2137587   .0603386    -3.54   0.000    -.3322526   -.0952648
         ew_ledig |   .2049871   .0823491     2.49   0.013     .0432686    .3667056
       ew_married |   .2029973   .0817612     2.48   0.013     .0424333    .3635612
        wb_anteil |  -.2394131   .0235855   -10.15   0.000    -.2857306   -.1930955
          wb_ausl |  -.0670326   .0145301    -4.61   0.000     -.095567   -.0384982
         wb_18t24 |  -.0234775   .0277753    -0.85   0.398    -.0780231    .0310682
         wb_25t34 |    .051083   .0194357     2.63   0.009     .0129148    .0892511
         wb_35t44 |   .0021706   .0249539     0.09   0.931    -.0468342    .0511754
         wb_45t59 |  -.0306734   .0212933    -1.44   0.150    -.0724895    .0111427
          avg_dur |   .0394388   .0247237     1.60   0.111    -.0091141    .0879916
          hh_kids |  -.0799158   .0424358    -1.88   0.060    -.1632519    .0034204
mpreis_flats_rent |  -.0143081   .0243231    -0.59   0.557    -.0620743    .0334581
            _cons |   -19.6695   11.32884    -1.74   0.083    -41.91725    2.578248
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  42,    617) =      34.01
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4340
Number of clusters (sb_new)  =        618         Root MSE        =     1.6259

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .2319922   .3414626     0.68   0.497    -.4385776     .902562
        F6event_a |   .3926978   .2929275     1.34   0.181     -.182558    .9679536
        F5event_a |  -.2927997   .2867145    -1.02   0.308    -.8558542    .2702549
        F4event_a |  -.2015509   .1766974    -1.14   0.254    -.5485521    .1454503
        F3event_a |  -.2299299   .1586609    -1.45   0.148    -.5415107    .0816509
        F2event_a |   -.137548   .1386426    -0.99   0.322    -.4098166    .1347205
        L0event_a |  -.5168999   .1832379    -2.82   0.005    -.8767455   -.1570543
        L1event_a |  -.2503758   .2296142    -1.09   0.276    -.7012959    .2005444
        L2event_a |   .2577318   .2562603     1.01   0.315    -.2455164      .76098
        L3event_a |  -.1216842   .2648513    -0.46   0.646    -.6418035    .3984352
        L4event_a |   .3401279   .5191615     0.66   0.513    -.6794099    1.359666
        L5event_a |   .3517091   .6268655     0.56   0.575    -.8793396    1.582758
        L6event_a |  -.2701649   .8015451    -0.34   0.736    -1.844252    1.303922
        L7event_a |  -.1507262    .787622    -0.19   0.848    -1.697471    1.396019
        F7event_b |  -.2995558   .4225792    -0.71   0.479    -1.129424    .5303122
        F6event_b |  -.0266307    .393455    -0.07   0.946    -.7993041    .7460427
        F5event_b |    .077274    .385164     0.20   0.841    -.6791173    .8336652
        F4event_b |  -.2901505    .216972    -1.34   0.182    -.7162436    .1359426
        F3event_b |  -.1696212   .2456067    -0.69   0.490    -.6519477    .3127053
        F2event_b |  -.0959713    .215751    -0.44   0.657    -.5196665     .327724
        L0event_b |  -.6117872   .2751658    -2.22   0.027    -1.152162   -.0714121
        L1event_b |  -.3620922   .3003232    -1.21   0.228    -.9518719    .2276874
        L2event_b |   -.028018   .3376623    -0.08   0.934    -.6911247    .6350886
        L3event_b |   .7085985   .3873791     1.83   0.068    -.0521429     1.46934
        L4event_b |   1.429879   .5280066     2.71   0.007     .3929707    2.466787
        L5event_b |   1.521692   .5369617     2.83   0.005     .4671977    2.576186
        L6event_b |   2.653459   .6148256     4.32   0.000     1.446054    3.860863
        L7event_b |   1.546337   .5957662     2.60   0.010     .3763613    2.716312
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.151693   1.209515     1.78   0.076    -.2235718    4.526958
         ew_biodt |   .7631371   .0330665    23.08   0.000     .6982005    .8280737
        ew_dtmihi |  -.1641048   .0538903    -3.05   0.002    -.2699355   -.0582742
         ew_ledig |   .4020781   .0722868     5.56   0.000       .26012    .5440361
       ew_married |   .6233372   .0706125     8.83   0.000     .4846673    .7620071
        wb_anteil |  -.5304381    .025379   -20.90   0.000    -.5802778   -.4805983
          wb_ausl |  -.0518716   .0179781    -2.89   0.004    -.0871773   -.0165659
         wb_18t24 |  -.0496062   .0268732    -1.85   0.065    -.1023802    .0031679
         wb_25t34 |  -.0189023   .0173654    -1.09   0.277    -.0530048    .0152003
         wb_35t44 |  -.0056121   .0216957    -0.26   0.796    -.0482185    .0369943
         wb_45t59 |  -.0232575   .0205123    -1.13   0.257    -.0635399    .0170248
          avg_dur |    .015428   .0229875     0.67   0.502    -.0297153    .0605712
          hh_kids |  -.1121729   .0375786    -2.99   0.003    -.1859704   -.0383754
mpreis_flats_rent |   .0093022    .024891     0.37   0.709    -.0395791    .0581835
            _cons |  -2.488213   11.01718    -0.23   0.821    -24.12392     19.1475
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  42,    617) =       2.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2646
                                                  Adj R-squared   =     0.0926
                                                  Within R-sq.    =     0.0807
Number of clusters (sb_new)  =        618         Root MSE        =     0.1055

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  del_street_dist | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .0036249   .0158513     0.23   0.819    -.0275042     .034754
        F6event_a |  -.0072664   .0140461    -0.52   0.605    -.0348504    .0203177
        F5event_a |  -.0056899   .0125602    -0.45   0.651    -.0303558     .018976
        F4event_a |  -.0002153   .0052952    -0.04   0.968     -.010614    .0101835
        F3event_a |  -.0023286   .0055178    -0.42   0.673    -.0131645    .0085073
        F2event_a |   .0035283    .004095     0.86   0.389    -.0045135      .01157
        L0event_a |   .0996673   .0235994     4.22   0.000     .0533225    .1460121
        L1event_a |   .0029458   .0066956     0.44   0.660    -.0102031    .0160947
        L2event_a |   .0019843   .0085633     0.23   0.817    -.0148324     .018801
        L3event_a |  -.0084623   .0157085    -0.54   0.590    -.0393109    .0223863
        L4event_a |   .0083529   .0233486     0.36   0.721    -.0374994    .0542052
        L5event_a |   -.048946   .0258356    -1.89   0.059    -.0996823    .0017903
        L6event_a |  -.0145069   .0286078    -0.51   0.612    -.0706873    .0416736
        L7event_a |  -.0136711   .0614017    -0.22   0.824    -.1342527    .1069106
        F7event_b |  -.0049049   .0133614    -0.37   0.714    -.0311442    .0213345
        F6event_b |    -.00809   .0124244    -0.65   0.515    -.0324892    .0163092
        F5event_b |  -.0095456   .0118846    -0.80   0.422    -.0328848    .0137935
        F4event_b |  -.0038221   .0065555    -0.58   0.560    -.0166958    .0090516
        F3event_b |   .0033275   .0083871     0.40   0.692    -.0131432    .0197982
        F2event_b |  -.0003969   .0053997    -0.07   0.941    -.0110008    .0102071
        L0event_b |   .1569917   .0323621     4.85   0.000     .0934384     .220545
        L1event_b |  -.0013144   .0113742    -0.12   0.908    -.0236513    .0210225
        L2event_b |   .0001448      .0102     0.01   0.989    -.0198862    .0201758
        L3event_b |   .0097851   .0229125     0.43   0.669    -.0352108     .054781
        L4event_b |   .0718548   .0542685     1.32   0.186    -.0347186    .1784281
        L5event_b |   .0367694   .0177818     2.07   0.039     .0018492    .0716895
        L6event_b |   .0364443   .0138974     2.62   0.009     .0091524    .0637363
        L7event_b |   .0253363    .020897     1.21   0.226    -.0157016    .0663742
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -.0282265   .0684527    -0.41   0.680    -.1626551     .106202
         ew_biodt |  -.0012436   .0017628    -0.71   0.481    -.0047053    .0022182
        ew_dtmihi |  -.0039478   .0028764    -1.37   0.170    -.0095965    .0017009
         ew_ledig |  -.0014873    .006365    -0.23   0.815     -.013987    .0110123
       ew_married |  -.0061638   .0057909    -1.06   0.288    -.0175361    .0052084
        wb_anteil |   .0012582   .0015688     0.80   0.423    -.0018226     .004339
          wb_ausl |   .0007336   .0007927     0.93   0.355    -.0008231    .0022904
         wb_18t24 |   .0007296   .0016048     0.45   0.650    -.0024219    .0038811
         wb_25t34 |   .0011262   .0011678     0.96   0.335    -.0011671    .0034194
         wb_35t44 |  -.0003908   .0014189    -0.28   0.783    -.0031772    .0023957
         wb_45t59 |    .002016    .001132     1.78   0.075    -.0002071    .0042391
          avg_dur |  -.0009329   .0010986    -0.85   0.396    -.0030904    .0012246
          hh_kids |   .0031617    .002199     1.44   0.151    -.0011567    .0074801
mpreis_flats_rent |  -.0016162   .0016551    -0.98   0.329    -.0048665     .001634
            _cons |   .4882029   .6244629     0.78   0.435    -.7381275    1.714533
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  42,    617) =     285.70
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9384
                                                  Adj R-squared   =     0.9240
                                                  Within R-sq.    =     0.8900
Number of clusters (sb_new)  =        618         Root MSE        =     0.0712

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     treat_simple | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .0087005   .0114083     0.76   0.446    -.0137032    .0311042
        F6event_a |   .0065841   .0090961     0.72   0.469     -.011279    .0244472
        F5event_a |   .0029408   .0091206     0.32   0.747    -.0149703     .020852
        F4event_a |  -.0071813   .0049573    -1.45   0.148    -.0169165    .0025539
        F3event_a |  -.0081083   .0054214    -1.50   0.135    -.0187549    .0025382
        F2event_a |  -.0075005   .0042402    -1.77   0.077    -.0158274    .0008265
        L0event_a |   .8709506   .0107816    80.78   0.000     .8497774    .8921237
        L1event_a |  -.0119554   .0054759    -2.18   0.029     -.022709   -.0012018
        L2event_a |  -.0099083   .0067278    -1.47   0.141    -.0231204    .0033038
        L3event_a |   .0445834   .0169959     2.62   0.009     .0112065    .0779603
        L4event_a |  -.0024853   .0266267    -0.09   0.926    -.0547752    .0498047
        L5event_a |  -.0263098   .0301603    -0.87   0.383    -.0855391    .0329194
        L6event_a |   .0078523   .0171454     0.46   0.647     -.025818    .0415226
        L7event_a |  -.0558974   .0501709    -1.11   0.266    -.1544238    .0426289
        F7event_b |  -.0096871   .0102322    -0.95   0.344    -.0297813    .0104071
        F6event_b |  -.0047663   .0086338    -0.55   0.581    -.0217215     .012189
        F5event_b |  -.0120889   .0092657    -1.30   0.192     -.030285    .0061072
        F4event_b |    .004986   .0068925     0.72   0.470    -.0085496    .0185216
        F3event_b |   .0106714   .0093285     1.14   0.253     -.007648    .0289908
        F2event_b |  -.0061466   .0051882    -1.18   0.237    -.0163353    .0040421
        L0event_b |   .7556044   .0189762    39.82   0.000     .7183387    .7928701
        L1event_b |   .0004869   .0087172     0.06   0.955    -.0166321    .0176058
        L2event_b |   .0042198   .0066403     0.64   0.525    -.0088205    .0172602
        L3event_b |   .1039108   .0313724     3.31   0.001     .0423012    .1655204
        L4event_b |   .2483313   .1741081     1.43   0.154     -.093585    .5902477
        L5event_b |   .0547591   .0145774     3.76   0.000     .0261317    .0833865
        L6event_b |   .0644882    .012866     5.01   0.000     .0392216    .0897547
        L7event_b |  -.0026051   .0337053    -0.08   0.938    -.0687961     .063586
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |    .006627   .0372821     0.18   0.859    -.0665882    .0798423
         ew_biodt |   .0004057   .0013576     0.30   0.765    -.0022603    .0030717
        ew_dtmihi |  -.0000478   .0023744    -0.02   0.984    -.0047107    .0046152
         ew_ledig |  -.0015981   .0023618    -0.68   0.499    -.0062362      .00304
       ew_married |   -.004444   .0024483    -1.82   0.070     -.009252     .000364
        wb_anteil |   -.000427   .0010439    -0.41   0.683     -.002477    .0016229
          wb_ausl |  -.0008759    .000449    -1.95   0.052    -.0017577    5.82e-06
         wb_18t24 |   .0004659   .0010547     0.44   0.659    -.0016054    .0025371
         wb_25t34 |  -.0006065   .0006762    -0.90   0.370    -.0019344    .0007214
         wb_35t44 |   .0004448   .0008186     0.54   0.587    -.0011628    .0020524
         wb_45t59 |  -.0013679    .000759    -1.80   0.072    -.0028585    .0001226
          avg_dur |   -.000176   .0008165    -0.22   0.829    -.0017794    .0014274
          hh_kids |   .0001067    .001471     0.07   0.942    -.0027821    .0029956
mpreis_flats_rent |  -.0004468   .0010408    -0.43   0.668    -.0024907    .0015971
            _cons |   .2732518   .3453115     0.79   0.429    -.4048765    .9513802
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         // Test and plot differences in estimates
.         foreach q in turnout_urne_a turnout_pos_req_a turnout_tot_req_a {
  2.                 cap frame drop orsCI
  3.                 frame create orsCI s est lb ub
  4.                 
.                 estimates restore `q'
  5.                 forvalues s= -4/2 {
  6.                         if `s'<0{
  7.                                 local t=`s'*(-1)
  8.                                  lincom F`t'event_b-F`t'event_a
  9.                         }
 10.                         else {
 11.                                  lincom L`s'event_b-L`s'event_a
 12.                         }
 13.                          frame post orsCI (`s') (`r(estimate)') (`r(lb)') (`r(ub)')
 14.                 }       
 15.                 frame orsCI {
 16.                         isid s, sort
 17.                         format est lb ub %3.0f
 18. 
.                         list, noobs clean               
 19.                         graph twoway (connect est s , sort lcol(gray) ms(T) msize(2.5pt) mcol
> (black)) (rcap lb ub s, col(black)) ///
>                                 , xtitle("") ytitle("Difference between estimates", size(small))
>  xlabel(-4(1)2) xtitle("Election since reassignment") ///
>                                 xline(-0.5, lcol(black) lpat(solid)) yline(0, lcolor(gray) lpat(
> solid)) name(test_`q', replace) ///
>                                 legend(order(1 "Difference between estimates") pos(12))
 20.                 }               
 21.                 
.         }
(results turnout_urne_a are active now)

 ( 1)  - F4event_a + F4event_b = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.3726992   .2801755    -1.33   0.184    -.9229124    .1775139
------------------------------------------------------------------------------

 ( 1)  - F3event_a + F3event_b = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.2012064   .2832303    -0.71   0.478    -.7574187     .355006
------------------------------------------------------------------------------

 ( 1)  - F2event_a + F2event_b = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3404583   .2115482     1.61   0.108    -.0749834    .7559001
------------------------------------------------------------------------------

 ( 1)  - o.F1event_a + o.F1event_b = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event_a + L0event_b = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.7504914   .3856517    -1.95   0.052     -1.50784    .0068576
------------------------------------------------------------------------------

 ( 1)  - L1event_a + L1event_b = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -1.058303   .4185035    -2.53   0.012    -1.880167    -.236439
------------------------------------------------------------------------------

 ( 1)  - L2event_a + L2event_b = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.6374995   .4582491    -1.39   0.165    -1.537417    .2624176
------------------------------------------------------------------------------
(data now sorted by s)

     s   est   lb   ub  
    -4    -0   -1    0  
    -3    -0   -1    0  
    -2     0   -0    1  
    -1     0    .    .  
     0    -1   -2    0  
     1    -1   -2   -0  
     2    -1   -2    0  
(results turnout_pos_req_a are active now)

 ( 1)  - F4event_a + F4event_b = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2840984   .2345436     1.21   0.226    -.1765021    .7446988
------------------------------------------------------------------------------

 ( 1)  - F3event_a + F3event_b = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2615141    .244656     1.07   0.286    -.2189452    .7419735
------------------------------------------------------------------------------

 ( 1)  - F2event_a + F2event_b = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.2988819   .1990562    -1.50   0.134    -.6897917    .0920279
------------------------------------------------------------------------------

 ( 1)  - o.F1event_a + o.F1event_b = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event_a + L0event_b = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6556032   .3790256     1.73   0.084    -.0887334     1.39994
------------------------------------------------------------------------------

 ( 1)  - L1event_a + L1event_b = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9465857   .4227127     2.24   0.025     .1164556    1.776716
------------------------------------------------------------------------------

 ( 1)  - L2event_a + L2event_b = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .351749    .456943     0.77   0.442    -.5456031    1.249101
------------------------------------------------------------------------------
(data now sorted by s)

     s   est   lb   ub  
    -4     0   -0    1  
    -3     0   -0    1  
    -2    -0   -1    0  
    -1     0    .    .  
     0     1   -0    1  
     1     1    0    2  
     2     0   -1    1  
(results turnout_tot_req_a are active now)

 ( 1)  - F4event_a + F4event_b = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0885996   .2587493    -0.34   0.732    -.5967356    .4195365
------------------------------------------------------------------------------

 ( 1)  - F3event_a + F3event_b = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0603087   .2758378     0.22   0.827    -.4813861    .6020036
------------------------------------------------------------------------------

 ( 1)  - F2event_a + F2event_b = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0415768    .239057     0.17   0.862    -.4278872    .5110407
------------------------------------------------------------------------------

 ( 1)  - o.F1event_a + o.F1event_b = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event_a + L0event_b = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0948873    .311005    -0.31   0.760    -.7056441    .5158694
------------------------------------------------------------------------------

 ( 1)  - L1event_a + L1event_b = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1117164   .3638639    -0.31   0.759    -.8262783    .6028454
------------------------------------------------------------------------------

 ( 1)  - L2event_a + L2event_b = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.2857498   .3935056    -0.73   0.468    -1.058523    .4870229
------------------------------------------------------------------------------
(data now sorted by s)

     s   est   lb   ub  
    -4    -0   -1    0  
    -3     0   -0    1  
    -2     0   -0    1  
    -1     0    .    .  
     0    -0   -1    1  
     1    -0   -1    1  
     2    -0   -1    0  

.                                 
.         
.         * PLOT: FIGURE C2. Effect Heterogeneity by Reassignment Reason  
.         graph combine turnout_urne test_turnout_urne_a, row(1)          imargins(small) xcommon 
> ycommon name(gr_urne, replace) ///
>                 title("{bf:Panel A.} Effect on Polling Place Turnout", nobox span bexpand justif
> ication(left) size(small) )

.         graph combine turnout_pos_req test_turnout_pos_req_a, row(1) imargins(small) xcommon yco
> mmon name(gr_pos, replace) ///
>                 title("{bf:Panel B.} Effect on Mail-in Turnout", nobox span bexpand justificatio
> n(left) size(small) )

.         graph combine turnout_tot_req test_turnout_tot_req_a, row(1) imargins(small) xcommon yco
> mmon name(gr_tot, replace) ///
>                 title("{bf:Panel C.} Effect on Total Turnout", nobox span bexpand justification(
> left) size(small) )

.         
.         graph combine gr_urne gr_pos gr_tot, xcommon col(1) imargins(small)

.         gr_edit .style.editstyle declared_ysize(6.5) editcopy

.         gr_edit .plotregion1.graph3.plotregion1.graph2.yaxis1.edit_tick 2 -0.5 `"-.5"', tickset(
> major)

.         gr_edit .plotregion1.graph3.plotregion1.graph2.yaxis1.edit_tick 3 0.5 `".5"', tickset(ma
> jor)    

. 
.         graph export "$figures/Figure_C2_ES_het_by_reason.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C2_ES_
    > het_by_reason.pdf saved as PDF format

. 
end of do-file
Running: 04e_rob_noveldid_figures_c1_d11_d12_table_e5.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
>         
> Output:
>         - Figure C.1, D.11, D.12
>         - Table E.5
>                 
> Main Tasks: 
>         * Estimate event study using novel DiD estimators (robustness check)
>         * Test of inattention  hypothesis: investigate sign and size 
>                 of t_1 - t_0 (change in turnout between period 1 and period 0 (event))
>                 
>                 
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
. ********************************************************************************
.         //       Prep Estimation //
. ********************************************************************************
. 
. // label outcomes for figure 
. lab var turnout_urne    "{bf:Panel A.} Effect on Polling Place Turnout"

. lab var turnout_pos_req "{bf:Panel B.} Effect on Mail-in Turnout"

. lab var turnout_tot_req "{bf:Panel C.} Effect on Total Turnout"

.         
. ********************************************************************************
. /*** Robustness to Novel DiD Estimates  (Figre C1)
>         
>         > Baseline Specification: smpl_trim=1,i.e., treat=100% reassignments, trim after "second
>  event", 
>         > No other sample Restriction
>         
>         Following commands required:
>                 - did_imputation (Borusyak et al. 2021): available on SSC
>                 - did_multiplegt (de Chaisemartin and D'Haultfoeuille 2020): available on SSC
>                 - eventstudyinteract (San and Abraham 2020): available on SSC
>                 - csdid (Callaway and Sant'Anna 2020): available on SSC
> */
. ********************************************************************************
. 
. 
. foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {     
  2.         
.         * BJS (2021) (weights possible)
.         did_imputation `v' sb_new wahl_id Ei if smpl_trim==1 [aw=wahlber_gesamt], autosample pre
> trends(4) horizons(0/2) cluster(sb_new) maxit(3000) controls($ctr)
  3.         estimates store bjs 
  4.         
.         * Estimation with did_multiplegt of de Chaisemartin and D'Haultfoeuille (2020) (NO weigh
> ts allowed)
.         did_multiplegt `v' sb_new wahl_id D  if smpl_trim==1, robust_dynamic dynamic(2) placebo(
> 4) firstdiff_placebo breps(100) seed(100) cluster(sb_new)  controls($ctr) covariances
  5. 
.         matrix dcdh_b = e(estimates) // storing the estimates
  6.         matrix dcdh_v = e(variances)
  7. 
.         * TWFE OLS (weights possible)
.         cap drop L* F*
  8.         forvalues l = 7(-1)1 {
  9.                 gen F`l'event = K==-`l'
 10.         }       
 11.         forvalues l = 0/7 {
 12.                 gen L`l'event = K==`l'
 13.         }       
 14.         order F1event, last
 15.         reghdfe `v' F7event-L7event F1event $ctr [aw=wahlber_gesamt] if smpl_trim ==1, absorb
> (i.wahl_id i.sb_new) cluster(sb_new)
 16.         estimates store ols     
 17.         
.         
.         * Estimation with eventstudyinteract of Sun and Abraham (2020) (weights allowed)
.         cap drop L* F*
 18.         cap drop lastcohort
 19.         sum Ei
 20.         gen lastcohort = Ei==r(max) // dummy for the latest- or never-treated cohort
 21.         forvalues l = 7(-1)1 {
 22.                 gen F`l'event = K==-`l'
 23.         }       
 24.         forvalues l = 0/7 {
 25.                 gen L`l'event = K==`l'
 26.         }
 27.         order F1event, last
 28.         eventstudyinteract `v' F7event-L7event F1event if smpl_trim==1 [aw=wahlber_gesamt], /
> //
>                 vce(cluster sb_new) absorb(i.wahl_id i.sb_new) cohort(Ei) control_cohort(lastcoh
> ort) covariates($ctr)
 29.         
.         matrix sa_b = e(b_iw) // storing the estimates
 30.         matrix sa_v = e(V_iw)   
 31. 
.         * Estimation with csdid of Callaway and Sant'Anna (2020) (weights allowed)      
.         cap drop gvar
 32.         gen gvar = cond(Ei==., 0, Ei)                                   
 33.         csdid `v' $ctr  if smpl_trim==1 [w=wahlber_gesamt] , ivar(sb_new) time(wahl_id) gvar(
> gvar) notyet agg(event) method(dripw)
 34.         estat event, estore(cs)                                                              
>            
 35.                                                                 
. *** Combine all plots using the stored estimates
.         event_plot  bjs dcdh_b#dcdh_v cs ols sa_b#sa_v , ///
>         stub_lag(tau# Effect_# Tp# L#event L#event ) stub_lead(pre# Placebo_# Tm# F#event F#even
> t ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.325(0.13)0.325) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(title("`:var lab `v''", nobox span bexpand justification(left) size(medium)) /
> //
>                 xtitle("Election since reassignment", size(medsmall)) ///
>                 ytitle("Voter turnout in %""(estimates)") xlabel(-4(1)2) ///
>                 legend(pos(6) order(1 "Borusyak et al. (2022)" 3 "de Chaisemartin & D'Haultfoeui
> lle (2020)" 5 "Callaway & Sant'Anna (2021)" 7 "TWFE OLS" 9 "Sun & Abraham (2021)") col(2) region
> (style(none)) size(vsmall)) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid))  y
> label(, angle(horizontal)) ///
>                 name(`v',replace) ) ///
>         lag_opt1(msymbol(+)     msize(3pt) color(black))                lag_ci_opt1(color(black)
> ) ///
>         lag_opt2(msymbol(O)     msize(3pt) color(midblue))              lag_ci_opt2(color(midblu
> e)) ///
>         lag_opt3(msymbol(Dh)    msize(3pt) color(dkorange))     lag_ci_opt3(color(dkorange)) ///
>         lag_opt4(msymbol(Th)    msize(3pt) color(teal))                 lag_ci_opt4(color(teal))
>  ///
>         lag_opt5(msymbol(Sh)    msize(3pt) color(cranberry))    lag_ci_opt5(color(cranberry)) 
 36. }
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 4,504
-----------------------------------------------------------------------------------
     turnout_urne | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |  -1.120276   .2102705    -5.33   0.000    -1.532399   -.7081535
             tau1 |  -.9756282   .2485465    -3.93   0.000     -1.46277    -.488486
             tau2 |  -.8906741   .2845491    -3.13   0.002     -1.44838   -.3329682
             pre1 |   .0413124   .2731125     0.15   0.880    -.4939783    .5766031
             pre2 |    .171769   .2478697     0.69   0.488    -.3140467    .6575847
             pre3 |  -.0598502   .2318466    -0.26   0.796    -.5142612    .3945608
             pre4 |  -.1186383   .1903345    -0.62   0.533    -.4916872    .2544105
        ln_ew_ges |  -.9746532    1.23076    -0.79   0.428    -3.386898    1.437592
         ew_biodt |   .3754808   .0305556    12.29   0.000      .315593    .4353686
        ew_dtmihi |   .0902978   .0593101     1.52   0.128    -.0259479    .2065435
         ew_ledig |   .1909613   .0730943     2.61   0.009     .0476992    .3342235
       ew_married |   .3619502   .0727541     4.97   0.000     .2193548    .5045457
        wb_anteil |  -.2438196   .0196824   -12.39   0.000    -.2823964   -.2052429
          wb_ausl |    .035678   .0153052     2.33   0.020     .0056802    .0656757
         wb_18t24 |  -.0485349    .033005    -1.47   0.141    -.1132236    .0161538
         wb_25t34 |  -.0111408   .0189145    -0.59   0.556    -.0482125     .025931
         wb_35t44 |  -.0262118   .0257015    -1.02   0.308    -.0765857    .0241622
         wb_45t59 |   .0438784   .0255879     1.71   0.086    -.0062729    .0940297
          avg_dur |  -.0448174   .0236401    -1.90   0.058    -.0911511    .0015164
          hh_kids |   .0166734   .0451465     0.37   0.712    -.0718121     .105159
mpreis_flats_rent |   .0784975   .0239418     3.28   0.001     .0315725    .1254226
-----------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -1.189084   .2139592  -1.608444  -.7697238       2399        270 
    Effect_1 | -1.098002   .2652554  -1.617902  -.5781013       1871        167 
    Effect_2 | -1.043706   .3134161  -1.658001  -.4294103       1265        124 
     Average | -1.129837   .2142332  -1.549734  -.7099397       5535        561 
   Placebo_1 | -.1213616    .128089   -.372416   .1296929       2399        270 
   Placebo_2 |  .3485214   .1539666   .0467469   .6502958       1791        260 
   Placebo_3 |   -.02135   .1261241  -.2685531   .2258532       1791        260 
   Placebo_4 | -.0538475   .1946355  -.4353331   .3276381       1193         87 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.69
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9633
                                                  Adj R-squared   =     0.9573
                                                  Within R-sq.    =     0.1478
Number of clusters (sb_new)  =        618         Root MSE        =     1.8963

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.2719899   .3783767    -0.72   0.473    -1.015052    .4710724
          F6event |  -.1600481   .3381397    -0.47   0.636    -.8240923    .5039962
          F5event |    .106833   .2699344     0.40   0.692    -.4232686    .6369345
          F4event |  -.1151478   .1961054    -0.59   0.557    -.5002627    .2699671
          F3event |  -.0375634   .2066195    -0.18   0.856     -.443326    .3681993
          F2event |   .1549506   .1416316     1.09   0.274    -.1231878     .433089
          L0event |  -1.068154   .2422044    -4.41   0.000    -1.543799   -.5925095
          L1event |  -.8724909   .2540048    -3.43   0.001     -1.37131   -.3736721
          L2event |  -.7015394    .270616    -2.59   0.010     -1.23298   -.1700993
          L3event |  -.0880174   .2711036    -0.32   0.746     -.620415    .4443802
          L4event |  -.1994726   .4709713    -0.42   0.672    -1.124374    .7254285
          L5event |   .4861285   .7253154     0.67   0.503    -.9382577    1.910515
          L6event |   .4275625   .8684947     0.49   0.623    -1.278002    2.133127
          L7event |   .9091168   1.138177     0.80   0.425    -1.326054    3.144287
          F1event |          0  (omitted)
        ln_ew_ges |  -.7462869   1.122614    -0.66   0.506    -2.950895    1.458321
         ew_biodt |   .3520456   .0289625    12.16   0.000     .2951686    .4089226
        ew_dtmihi |   .0613483   .0527968     1.16   0.246    -.0423348    .1650315
         ew_ledig |    .222933   .0597345     3.73   0.000     .1056254    .3402406
       ew_married |   .4248988    .061581     6.90   0.000      .303965    .5458326
        wb_anteil |  -.2432859   .0196388   -12.39   0.000    -.2818528   -.2047189
          wb_ausl |   .0212218   .0149923     1.42   0.157    -.0082203    .0506639
         wb_18t24 |  -.0462596   .0292086    -1.58   0.114      -.10362    .0111007
         wb_25t34 |  -.0432859   .0168545    -2.57   0.010    -.0763851   -.0101868
         wb_35t44 |    .002344   .0215326     0.11   0.913     -.039942    .0446301
         wb_45t59 |    .015456   .0219157     0.71   0.481    -.0275824    .0584944
          avg_dur |  -.0193425   .0223354    -0.87   0.387     -.063205      .02452
          hh_kids |  -.0496397   .0436321    -1.14   0.256    -.1353252    .0360458
mpreis_flats_rent |   .0512944   .0196556     2.61   0.009     .0126944    .0898944
            _cons |   8.087133   10.18894     0.79   0.428    -11.92207    28.09634
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 6,754,095.5338926)
(obs=4,346)

IW estimates for dynamic effects                        Number of obs =  4,666
Absorbing 2 HDFE groups                                 F(49, 617)    =  19.53
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.9641
                                                        Adj R-squared = 0.9580
                                                        Root MSE      = 1.8813
                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |  -.0869626   .9131658    -0.10   0.924    -1.880252    1.706327
     F5event |   .7137243   .4408209     1.62   0.106     -.151967    1.579416
     F4event |  -.3447923   .2156978    -1.60   0.110    -.7683832    .0787986
     F3event |  -.1816186   .2261147    -0.80   0.422    -.6256665    .2624292
     F2event |   .1802547   .1494861     1.21   0.228    -.1133085    .4738179
     L0event |  -1.456301   .2819517    -5.17   0.000    -2.010002   -.9025993
     L1event |  -1.141387   .2668296    -4.28   0.000    -1.665392   -.6173831
     L2event |  -.9316933    .275715    -3.38   0.001    -1.473147   -.3902396
     L3event |    -.33395   .2700672    -1.24   0.217    -.8643123    .1964122
     L4event |  -.1446342   .7282007    -0.20   0.843    -1.574687    1.285418
     L5event |   .6120677   .9608584     0.64   0.524    -1.274882    2.499017
     L6event |  -.6435543   1.557433    -0.41   0.680    -3.702067    2.414958
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 4,609
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |    .049038   .0470108     1.04   0.297    -.0431015    .1411775
    Post_avg |  -.9844984   .2595598    -3.79   0.000    -1.493226   -.4757704
         Tm6 |  -.0012069   .1927124    -0.01   0.995    -.3789163    .3765025
         Tm5 |   .4540845   .2653519     1.71   0.087    -.0659957    .9741647
         Tm4 |  -.1592715   .1762558    -0.90   0.366    -.5047265    .1861835
         Tm3 |  -.0752085   .1141524    -0.66   0.510     -.298943     .148526
         Tm2 |   .1587597   .1417005     1.12   0.263    -.1189682    .4364875
         Tm1 |  -.0829294    .114889    -0.72   0.470    -.3081077    .1422489
         Tp0 |  -1.016045   .2327383    -4.37   0.000    -1.472204    -.559886
         Tp1 |  -1.052231   .2778517    -3.79   0.000    -1.596811    -.507652
         Tp2 |  -.8667574   .3198828    -2.71   0.007    -1.493716   -.2397985
         Tp3 |  -.6591636   .3123538    -2.11   0.035    -1.271366   -.0469615
         Tp4 |  -.7073051   .6593844    -1.07   0.283    -1.999675    .5850645
         Tp5 |  -1.605488   .7975169    -2.01   0.044    -3.168592   -.0423835
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |    .049038   .0470108     1.04   0.297    -.0431015    .1411775
    Post_avg |  -.9844984   .2595598    -3.79   0.000    -1.493226   -.4757704
         Tm6 |  -.0012069   .1927124    -0.01   0.995    -.3789163    .3765025
         Tm5 |   .4540845   .2653519     1.71   0.087    -.0659957    .9741647
         Tm4 |  -.1592715   .1762558    -0.90   0.366    -.5047265    .1861835
         Tm3 |  -.0752085   .1141524    -0.66   0.510     -.298943     .148526
         Tm2 |   .1587597   .1417005     1.12   0.263    -.1189682    .4364875
         Tm1 |  -.0829294    .114889    -0.72   0.470    -.3081077    .1422489
         Tp0 |  -1.016045   .2327383    -4.37   0.000    -1.472204    -.559886
         Tp1 |  -1.052231   .2778517    -3.79   0.000    -1.596811    -.507652
         Tp2 |  -.8667574   .3198828    -2.71   0.007    -1.493716   -.2397985
         Tp3 |  -.6591636   .3123538    -2.11   0.035    -1.271366   -.0469615
         Tp4 |  -.7073051   .6593844    -1.07   0.283    -1.999675    .5850645
         Tp5 |  -1.605488   .7975169    -2.01   0.044    -3.168592   -.0423835
------------------------------------------------------------------------------
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 4,504
-----------------------------------------------------------------------------------
  turnout_pos_req | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |   .7272347   .2025764     3.59   0.000     .3301922    1.124277
             tau1 |   1.010544   .2349639     4.30   0.000     .5500228    1.471065
             tau2 |   1.268263   .2955623     4.29   0.000     .6889718    1.847555
             pre1 |   .2461122   .2512753     0.98   0.327    -.2463783    .7386027
             pre2 |   .0446764   .2547514     0.18   0.861    -.4546272    .5439801
             pre3 |   .1715935   .2603506     0.66   0.510    -.3386844    .6818714
             pre4 |   .1291609   .1763081     0.73   0.464    -.2163966    .4747183
        ln_ew_ges |   1.976513   1.619217     1.22   0.222    -1.197094     5.15012
         ew_biodt |   .4421471   .0342319    12.92   0.000     .3750538    .5092403
        ew_dtmihi |  -.1937054   .0716666    -2.70   0.007    -.3341694   -.0532414
         ew_ledig |   .2036921   .0888851     2.29   0.022     .0294805    .3779037
       ew_married |   .2270757   .0896022     2.53   0.011     .0514586    .4026929
        wb_anteil |   -.308242   .0284066   -10.85   0.000    -.3639179   -.2525661
          wb_ausl |  -.0496714   .0155546    -3.19   0.001    -.0801579   -.0191849
         wb_18t24 |   -.019432   .0324706    -0.60   0.550    -.0830732    .0442092
         wb_25t34 |   .0245668   .0189091     1.30   0.194    -.0124943    .0616279
         wb_35t44 |   .0156343   .0269277     0.58   0.562    -.0371431    .0684118
         wb_45t59 |  -.0299781   .0243672    -1.23   0.219     -.077737    .0177807
          avg_dur |   .0757808   .0284315     2.67   0.008      .020056    .1315055
          hh_kids |  -.0848814   .0527506    -1.61   0.108    -.1882706    .0185078
mpreis_flats_rent |   .0232245   .0274648     0.85   0.398    -.0306055    .0770546
-----------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .7195546    .209301   .3093245   1.129785       2399        270 
    Effect_1 |  1.104612   .2665072   .5822578   1.626966       1871        167 
    Effect_2 |  1.324218   .3052482   .7259313   1.922504       1265        124 
     Average |  .9678305    .211178   .5539216   1.381739       5535        561 
   Placebo_1 |  .1518878   .1372492  -.1171205   .4208962       2399        270 
   Placebo_2 | -.1605939   .1990393  -.5507109   .2295231       1791        260 
   Placebo_3 |  .1338749   .1604953  -.1806958   .4484457       1791        260 
   Placebo_4 |  .3688683   .2078513  -.0385202   .7762568       1193         87 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      15.68
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9457
                                                  Adj R-squared   =     0.9369
                                                  Within R-sq.    =     0.2003
Number of clusters (sb_new)  =        618         Root MSE        =     1.9547

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .276988   .3092727     0.90   0.371    -.3303667    .8843427
          F6event |   .3148414   .2770009     1.14   0.256    -.2291375    .8588203
          F5event |  -.3360842   .2820305    -1.19   0.234    -.8899403    .2177719
          F4event |  -.1089699   .1694836    -0.64   0.520    -.4418045    .2238647
          F3event |  -.1143589   .2016849    -0.57   0.571    -.5104309    .2817132
          F2event |  -.1662226   .1429373    -1.16   0.245    -.4469252    .1144801
          L0event |   .5381031   .2339631     2.30   0.022     .0786427    .9975636
          L1event |   .8706087   .2415275     3.60   0.000     .3962931    1.344924
          L2event |   .9679545   .2802916     3.45   0.001     .4175133    1.518396
          L3event |   .0769893    .269978     0.29   0.776    -.4531979    .6071765
          L4event |   1.629122   .6201424     2.63   0.009     .4112765    2.846968
          L5event |   .9910967   .5551212     1.79   0.075    -.0990593    2.081253
          L6event |   .0056946   .5736399     0.01   0.992    -1.120829    1.132218
          L7event |   -.400396   .9271444    -0.43   0.666    -2.221137    1.420345
          F1event |          0  (omitted)
        ln_ew_ges |   2.396505   1.412975     1.70   0.090    -.3783192    5.171328
         ew_biodt |   .4381682   .0312317    14.03   0.000     .3768348    .4995016
        ew_dtmihi |  -.2278822    .060953    -3.74   0.000    -.3475827   -.1081817
         ew_ledig |     .16008   .0853711     1.88   0.061    -.0075731     .327733
       ew_married |   .1907753   .0848466     2.25   0.025     .0241523    .3573984
        wb_anteil |  -.2980442    .026516   -11.24   0.000    -.3501167   -.2459717
          wb_ausl |   -.056026   .0139204    -4.02   0.000    -.0833631    -.028689
         wb_18t24 |  -.0102823   .0285045    -0.36   0.718    -.0662599    .0456953
         wb_25t34 |   .0315455   .0168646     1.87   0.062    -.0015734    .0646644
         wb_35t44 |  -.0088774   .0224818    -0.39   0.693    -.0530276    .0352729
         wb_45t59 |   -.027485   .0205433    -1.34   0.181    -.0678283    .0128583
          avg_dur |   .0609851   .0243414     2.51   0.012      .013183    .1087872
          hh_kids |  -.0405334   .0434366    -0.93   0.351    -.1258349    .0447681
mpreis_flats_rent |   .0523958   .0213153     2.46   0.014     .0105364    .0942552
            _cons |  -9.380828    12.1834    -0.77   0.442    -33.30678    14.54513
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 6,754,095.5338926)
(obs=4,346)

IW estimates for dynamic effects                        Number of obs =  4,666
Absorbing 2 HDFE groups                                 F(49, 617)    =  13.43
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.9470
                                                        Adj R-squared = 0.9381
                                                        Root MSE      = 1.9358
                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |   .1394359   .5785461     0.24   0.810    -.9967223    1.275594
     F5event |  -.3508032   .3996984    -0.88   0.380    -1.135737     .434131
     F4event |   .1011215   .1670063     0.61   0.545    -.2268483    .4290913
     F3event |   .0161718   .2200177     0.07   0.941    -.4159026    .4482461
     F2event |    -.22607   .1572705    -1.44   0.151    -.5349204    .0827804
     L0event |    .994885   .2583689     3.85   0.000     .4874959    1.502274
     L1event |   1.028146   .2505314     4.10   0.000     .5361485    1.520144
     L2event |   1.109717   .2785072     3.98   0.000     .5627805    1.656654
     L3event |   .1789858   .2587014     0.69   0.489    -.3290561    .6870278
     L4event |   1.084574   .6977203     1.55   0.121    -.2856201    2.454769
     L5event |   .3024585   .8925454     0.34   0.735    -1.450337    2.055254
     L6event |   .5533374   .8946855     0.62   0.536    -1.203661    2.310335
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 4,609
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |  -.0483605    .043583    -1.11   0.267    -.1337816    .0370606
    Post_avg |   .2484956   .3707866     0.67   0.503    -.4782328     .975224
         Tm6 |   .1682019   .1534555     1.10   0.273    -.1325654    .4689693
         Tm5 |  -.5944373   .2670999    -2.23   0.026    -1.117944   -.0709311
         Tm4 |   .0521035   .1494386     0.35   0.727    -.2407908    .3449977
         Tm3 |   .1538283   .1024463     1.50   0.133    -.0469627    .3546193
         Tm2 |    .030953   .1333389     0.23   0.816    -.2303863    .2922924
         Tm1 |  -.1008125   .1095401    -0.92   0.357    -.3155072    .1138823
         Tp0 |   .5552407   .2045589     2.71   0.007     .1543125    .9561688
         Tp1 |   .9329878   .2457422     3.80   0.000      .451342    1.414634
         Tp2 |    .948583   .2948746     3.22   0.001     .3706394    1.526527
         Tp3 |    .417298    .301038     1.39   0.166    -.1727258    1.007322
         Tp4 |   .4557682   1.017482     0.45   0.654     -1.53846    2.449997
         Tp5 |  -1.818904   1.264403    -1.44   0.150    -4.297089    .6592803
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |  -.0483605    .043583    -1.11   0.267    -.1337816    .0370606
    Post_avg |   .2484956   .3707866     0.67   0.503    -.4782328     .975224
         Tm6 |   .1682019   .1534555     1.10   0.273    -.1325654    .4689693
         Tm5 |  -.5944373   .2670999    -2.23   0.026    -1.117944   -.0709311
         Tm4 |   .0521035   .1494386     0.35   0.727    -.2407908    .3449977
         Tm3 |   .1538283   .1024463     1.50   0.133    -.0469627    .3546193
         Tm2 |    .030953   .1333389     0.23   0.816    -.2303863    .2922924
         Tm1 |  -.1008125   .1095401    -0.92   0.357    -.3155072    .1138823
         Tp0 |   .5552407   .2045589     2.71   0.007     .1543125    .9561688
         Tp1 |   .9329878   .2457422     3.80   0.000      .451342    1.414634
         Tp2 |    .948583   .2948746     3.22   0.001     .3706394    1.526527
         Tp3 |    .417298    .301038     1.39   0.166    -.1727258    1.007322
         Tp4 |   .4557682   1.017482     0.45   0.654     -1.53846    2.449997
         Tp5 |  -1.818904   1.264403    -1.44   0.150    -4.297089    .6592803
------------------------------------------------------------------------------
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 4,504
-----------------------------------------------------------------------------------
  turnout_tot_req | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |  -.3930419   .1473939    -2.67   0.008    -.6819287   -.1041552
             tau1 |   .0349157   .2067212     0.17   0.866    -.3702503    .4400818
             tau2 |   .3775896   .2630113     1.44   0.151     -.137903    .8930823
             pre1 |   .2874246   .2525192     1.14   0.255     -.207504    .7823531
             pre2 |   .2164459   .2205141     0.98   0.326    -.2157539    .6486457
             pre3 |   .1117432   .2063486     0.54   0.588    -.2926926     .516179
             pre4 |   .0105229   .1904191     0.06   0.956    -.3626917    .3837375
        ln_ew_ges |   1.001861   1.159435     0.86   0.388    -1.270589    3.274311
         ew_biodt |   .8176278   .0351937    23.23   0.000     .7486494    .8866062
        ew_dtmihi |  -.1034075   .0601796    -1.72   0.086    -.2213573    .0145423
         ew_ledig |   .3946536   .0760123     5.19   0.000     .2456722    .5436351
       ew_married |   .5890262   .0769452     7.66   0.000     .4382164    .7398359
        wb_anteil |  -.5520616   .0293764   -18.79   0.000    -.6096383   -.4944849
          wb_ausl |  -.0139934   .0141476    -0.99   0.323    -.0417222    .0137354
         wb_18t24 |  -.0679669   .0309245    -2.20   0.028    -.1285777   -.0073561
         wb_25t34 |    .013426   .0173488     0.77   0.439     -.020577     .047429
         wb_35t44 |  -.0105774   .0236768    -0.45   0.655    -.0569832    .0358284
         wb_45t59 |   .0139003   .0240823     0.58   0.564    -.0333002    .0611008
          avg_dur |   .0309634   .0268618     1.15   0.249    -.0216847    .0836115
          hh_kids |  -.0682081   .0404027    -1.69   0.091    -.1473959    .0109798
mpreis_flats_rent |    .101722   .0258864     3.93   0.000     .0509856    .1524585
-----------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.4695288   .1645955  -.7921361  -.1469215       2399        270 
    Effect_1 |  .0066108   .2316865  -.4474947   .4607164       1871        167 
    Effect_2 |  .2805124   .2865483  -.2811222    .842147       1265        124 
     Average | -.1620058   .1787891  -.5124323   .1884208       5535        561 
   Placebo_1 |   .030526   .1358257  -.2356923   .2967443       2399        270 
   Placebo_2 |  .1879276   .1553472  -.1165529   .4924082       1791        260 
   Placebo_3 |  .1125244   .1327576  -.1476804   .3727292       1791        260 
   Placebo_4 |  .3150211   .2693467  -.2128984   .8429406       1193         87 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      45.00
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9875
                                                  Adj R-squared   =     0.9855
                                                  Within R-sq.    =     0.4457
Number of clusters (sb_new)  =        618         Root MSE        =     1.7997

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0049977   .3288787     0.02   0.988    -.6408596     .650855
          F6event |   .1547925   .2763992     0.56   0.576    -.3880047    .6975897
          F5event |  -.2292509   .2737864    -0.84   0.403     -.766917    .3084152
          F4event |  -.2241174   .1715177    -1.31   0.192    -.5609465    .1127118
          F3event |  -.1519224   .1677705    -0.91   0.366    -.4813929    .1775481
          F2event |  -.0112714   .1532364    -0.07   0.941    -.3121996    .2896568
          L0event |  -.5300509    .167397    -3.17   0.002    -.8587878   -.2013139
          L1event |  -.0018816   .2026481    -0.01   0.993    -.3998451     .396082
          L2event |   .2664154   .2414528     1.10   0.270    -.2077536    .7405844
          L3event |  -.0110278   .2511612    -0.04   0.965    -.5042622    .4822066
          L4event |   1.429649   .7515148     1.90   0.058    -.0461877    2.905486
          L5event |   1.477225   .6837042     2.16   0.031     .1345559    2.819895
          L6event |   .4332568   .9361461     0.46   0.644    -1.405162    2.271676
          L7event |   .5087222   .8213052     0.62   0.536     -1.10417    2.121615
          F1event |          0  (omitted)
        ln_ew_ges |   1.650218   1.072406     1.54   0.124    -.4557902    3.756225
         ew_biodt |   .7902138   .0329271    24.00   0.000      .725551    .8548767
        ew_dtmihi |  -.1665338   .0542449    -3.07   0.002    -.2730608   -.0600067
         ew_ledig |   .3830132   .0745097     5.14   0.000     .2366899    .5293365
       ew_married |   .6156742   .0733212     8.40   0.000     .4716849    .7596636
        wb_anteil |    -.54133   .0277962   -19.47   0.000    -.5959166   -.4867435
          wb_ausl |  -.0348043   .0125578    -2.77   0.006    -.0594655   -.0101431
         wb_18t24 |   -.056542   .0267672    -2.11   0.035    -.1091079    -.003976
         wb_25t34 |  -.0117404   .0144449    -0.81   0.417    -.0401076    .0166268
         wb_35t44 |  -.0065333   .0192077    -0.34   0.734    -.0442536     .031187
         wb_45t59 |   -.012029   .0192847    -0.62   0.533    -.0499007    .0258427
          avg_dur |   .0416426   .0224601     1.85   0.064    -.0024648      .08575
          hh_kids |  -.0901732   .0367571    -2.45   0.014    -.1623574    -.017989
mpreis_flats_rent |   .1036902    .020883     4.97   0.000     .0626798    .1447006
            _cons |  -1.293706   10.38581    -0.12   0.901    -21.68953    19.10211
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 6,754,095.5338926)
(obs=4,346)

IW estimates for dynamic effects                        Number of obs =  4,666
Absorbing 2 HDFE groups                                 F(49, 617)    =  31.78
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.9877
                                                        Adj R-squared = 0.9856
                                                        Root MSE      = 1.7950
                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |   .0524739   .7296876     0.07   0.943    -1.380498    1.485446
     F5event |   .3629226    .364485     1.00   0.320     -.352859    1.078704
     F4event |  -.2436704   .1842347    -1.32   0.186    -.6054736    .1181327
     F3event |   -.165447   .1766601    -0.94   0.349    -.5123751    .1814811
     F2event |  -.0458148   .1839657    -0.25   0.803    -.4070896      .31546
     L0event |  -.4614153   .1900539    -2.43   0.015    -.8346462   -.0881844
     L1event |  -.1132408   .2130431    -0.53   0.595    -.5316184    .3051367
     L2event |   .1780244   .2424245     0.73   0.463    -.2980528    .6541016
     L3event |  -.1549642   .2670614    -0.58   0.562    -.6794237    .3694952
     L4event |   .9399394   .6225762     1.51   0.132    -.2826858    2.162565
     L5event |    .914526   .6726239     1.36   0.174    -.4063838    2.235436
     L6event |  -.0902185   1.339526    -0.07   0.946    -2.720802    2.540365
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 4,609
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0006776   .0494359     0.01   0.989     -.096215    .0975701
    Post_avg |  -.7360028   .3322218    -2.22   0.027    -1.387146     -.08486
         Tm6 |   .1669945   .1544601     1.08   0.280    -.1357417    .4697308
         Tm5 |  -.1403518   .3283565    -0.43   0.669    -.7839186    .5032151
         Tm4 |  -.1071677   .1885917    -0.57   0.570    -.4768006    .2624652
         Tm3 |   .0786193    .115217     0.68   0.495    -.1472018    .3044405
         Tm2 |   .1897133   .1323425     1.43   0.152    -.0696732    .4490998
         Tm1 |  -.1837423   .1168244    -1.57   0.116    -.4127139    .0452294
         Tp0 |  -.4608038   .1788825    -2.58   0.010    -.8114071   -.1102005
         Tp1 |   -.119243   .2422519    -0.49   0.623     -.594048    .3555621
         Tp2 |   .0818262   .2897874     0.28   0.778    -.4861468    .6497991
         Tp3 |  -.2418655   .3603783    -0.67   0.502    -.9481941    .4644631
         Tp4 |   -.251536   .8577133    -0.29   0.769    -1.932623    1.429551
         Tp5 |  -3.424394   1.351091    -2.53   0.011    -6.072483   -.7763056
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0006776   .0494359     0.01   0.989     -.096215    .0975701
    Post_avg |  -.7360028   .3322218    -2.22   0.027    -1.387146     -.08486
         Tm6 |   .1669945   .1544601     1.08   0.280    -.1357417    .4697308
         Tm5 |  -.1403518   .3283565    -0.43   0.669    -.7839186    .5032151
         Tm4 |  -.1071677   .1885917    -0.57   0.570    -.4768006    .2624652
         Tm3 |   .0786193    .115217     0.68   0.495    -.1472018    .3044405
         Tm2 |   .1897133   .1323425     1.43   0.152    -.0696732    .4490998
         Tm1 |  -.1837423   .1168244    -1.57   0.116    -.4127139    .0452294
         Tp0 |  -.4608038   .1788825    -2.58   0.010    -.8114071   -.1102005
         Tp1 |   -.119243   .2422519    -0.49   0.623     -.594048    .3555621
         Tp2 |   .0818262   .2897874     0.28   0.778    -.4861468    .6497991
         Tp3 |  -.2418655   .3603783    -0.67   0.502    -.9481941    .4644631
         Tp4 |   -.251536   .8577133    -0.29   0.769    -1.932623    1.429551
         Tp5 |  -3.424394   1.351091    -2.53   0.011    -6.072483   -.7763056
------------------------------------------------------------------------------

. 
.         * PLOT: FIGURE C1. Sensitivity to Different Estimators
.         grc1leg  turnout_urne turnout_pos_req turnout_tot_req , xcommon  col(2) iscale(.65) imar
> gins(small) pos(12)

.         gr_edit .style.editstyle declared_ysize(4) editcopy

.         gr_edit .legend.DragBy -60 40

.         gr_edit .legend.Edit , style(cols(1)) style(rows(0)) keepstyles

.         gr_edit .legend.title.text.Arrpush "Estimator:"

.         gr_edit .legend.title.style.editstyle size(small) editcopy

.         gr_edit .legend.title.DragBy 0 -15 

.         graph export "$figures/Figure_C1_ES_novel_ctr_wgt.pdf", replace 
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C1_ES_
    > novel_ctr_wgt.pdf saved as PDF format

. 
. 
.  
. 
. ********************************************************************************
.  // Test of Mechanism: ES  on sample with distance INCREASES + Never treated (Figure D11) //
. ********************************************************************************
. 
. 
. // create frame to post test results to
. cap frame drop   testsUp

.         frame create testsUp str10 estimator str20 turnout str20 delta double(p t)

.         
. foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {     
  2.         
.                 
.         * BJS (2021) (weights possible)
.         did_imputation `v' sb_new wahl_id Ei if smpl_trim_dist==1 [aw=wahlber_gesamt], autosampl
> e pretrends(4) horizons(0/2) cluster(sb_new) maxit(3000) controls($ctr)
  3.         estimates store bjs 
  4.         
.         // test: t_1 - t_0
.         lincom tau1-tau0        
  5.         local diff = "`:di %8.3f `r(estimate)''"
  6.         stars `r(p)',   stat(p)
  7.         local delta "`diff'`r(stars)'"
  8.         
.         frame post testsUp ("BJS (2021)") ("`v'") ("`delta'") (`r(p)') (.)
  9. 
.         * Estimation with did_multiplegt of de Chaisemartin and D'Haultfoeuille (2020) (NO weigh
> ts allowed)
.         did_multiplegt `v' sb_new wahl_id D  if smpl_trim_dist==1, ///
>                 robust_dynamic dynamic(2) placebo(4) firstdiff_placebo breps(100) seed(100) clus
> ter(sb_new)  covariances controls($ctr) weight(wahlber_gesamt)
 10.         matrix dcdh_b = e(estimates) // storing the estimates
 11.         matrix dcdh_v = e(variances)
 12.         
.         // test: t_1 - t_0      
.         local  diff = e(effect_1)-e(effect_0) 
 13.         local diff = "`:di %8.3f `diff''"       
 14.         local  se = sqrt(e(se_effect_1)^2+e(se_effect_0)^2-2*e(cov_effects_0_1)) // SE of t_1
>  - t_0
 15.         local  tstat = `diff'/`se'
 16.         stars `tstat',  stat(t)
 17.         local delta "`diff'`r(stars)'"  
 18.         frame post testsUp ("dCDH (2020)") ("`v'") ("`delta'") (.) (`r(t)')
 19.         
.         
.         * TWFE OLS (weights possible)
.         cap drop L* F*
 20.         forvalues l = 7(-1)1 {
 21.                 gen F`l'event = K==-`l'
 22.         }       
 23.         forvalues l = 0/7 {
 24.                 gen L`l'event = K==`l'
 25.         }       
 26.                 reghdfe `v' F7event-F2event L0event-L7event F1event $ctr [aw=wahlber_gesamt] 
> if smpl_trim_dist ==1, absorb(i.wahl_id i.sb_new) cluster(sb_new)
 27.         estimates store ols     
 28.         
.         // test: t_1 - t_0
.         lincom L1event-L0event
 29.         local diff = "`:di %8.3f `r(estimate)''"
 30.         stars `r(p)',   stat(p)
 31.         local delta "`diff'`r(stars)'"
 32.         frame post testsUp ("TWFE-OLS") ("`v'") ("`delta'") (`r(p)') (.)
 33.                 
.         
.         * Estimation with eventstudyinteract of Sun and Abraham (2020) (weights allowed)
.         cap drop L* F*
 34.         cap drop lastcohort
 35.         sum Ei
 36.         gen lastcohort = Ei==r(max) // dummy for the latest- or never-treated cohort
 37.         forvalues l = 7(-1)1 {
 38.                 gen F`l'event = K==-`l'
 39.         }       
 40.         forvalues l = 0/7 {
 41.                 gen L`l'event = K==`l'
 42.         }
 43.         order F1event, last
 44.         eventstudyinteract `v' F7event-L7event F1event  if smpl_trim_dist==1 [aw=wahlber_gesa
> mt], vce(cluster sb_new) absorb(sb_new wahl_id) cohort(Ei) control_cohort(lastcohort) covariates
> ($ctr)
 45.         
.         matrix sa_b = e(b_iw) 
 46.         matrix sa_v = e(V_iw)
 47.         
.         // test: t_1 - t_0
.          matrix b = e(b_iw)
 48.      matrix V = e(V_iw)
 49.      ereturn post b V
 50.         lincom L1event-L0event
 51.         local diff = "`:di %8.3f `r(estimate)''"
 52.         stars `r(p)',   stat(p)
 53.         local delta "`diff'`r(stars)'"
 54.         frame post testsUp ("SA (2020)") ("`v'") ("`delta'") (`r(p)') (.)
 55.                 
.         * Estimation with csdid of Callaway and Sant'Anna (2020) (weights allowed)      
.         cap drop gvar
 56.         gen gvar = cond(Ei==., 0, Ei)                                   
 57.         csdid `v'  if smpl_trim_dist==1 [w=wahlber_gesamt] , ivar(sb_new) time(wahl_id) gvar(
> gvar) notyet agg(event) method(dripw)
 58.         estat event, estore(cs)                                                              
>            
 59.                                 
.         // test: t_1 - t_0
.         local diff      = e(b)[1,10]-e(b)[1,9]
 60.         local diff = "`:di %8.3f `diff''"
 61.         local se        = sqrt(e(V)[10,10]+e(V)[9,9]-2*e(V)[10,9]) // SE of t_1-t_0
 62.         local tstat = `diff'/`se'
 63.         stars `tstat',  stat(t)
 64.         local delta "`diff'`r(stars)'"
 65.         frame post testsUp ("CS (2021)") ("`v'") ("`delta'") (.) (`r(t)') 
 66.         
.                 
. *** Combine all plots using the stored estimates
. 
.         event_plot  bjs dcdh_b#dcdh_v cs ols sa_b#sa_v , ///
>         stub_lag(tau# Effect_# Tp# L#event L#event ) stub_lead(pre# Placebo_# Tm# F#event F#even
> t ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.325(0.13)0.325) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(title("`:var lab `v''", nobox span bexpand justification(left) size(medium)) /
> //
>                 xtitle("Election since reassignment", size(medsmall)) ///
>                 ytitle("Voter turnout in %""(estimates)") xlabel(-4(1)2) ///
>                 legend(pos(6) order(1 "Borusyak et al. (2022)" 3 "de Chaisemartin & D'Haultfoeui
> lle (2020)" 5 "Callaway & Sant'Anna (2021)" 7 "TWFE OLS" 9 "Sun & Abraham (2021)") col(2) region
> (style(none)) size(vsmall)) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid))  y
> label(, angle(horizontal)) ///
>                 name(`v',replace) ) ///
>         lag_opt1(msymbol(+)     msize(3pt) color(black))                lag_ci_opt1(color(black)
> ) ///
>         lag_opt2(msymbol(O)     msize(3pt) color(midblue))              lag_ci_opt2(color(midblu
> e)) ///
>         lag_opt3(msymbol(Dh)    msize(3pt) color(dkorange))     lag_ci_opt3(color(dkorange)) ///
>         lag_opt4(msymbol(Th)    msize(3pt) color(teal))                 lag_ci_opt4(color(teal))
>  ///
>         lag_opt5(msymbol(Sh)    msize(3pt) color(cranberry))    lag_ci_opt5(color(cranberry))
 67. }
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 3,816
-----------------------------------------------------------------------------------
     turnout_urne | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |  -2.143661    .233315    -9.19   0.000     -2.60095   -1.686372
             tau1 |  -2.313007     .28132    -8.22   0.000    -2.864384    -1.76163
             tau2 |  -2.014152   .3238655    -6.22   0.000    -2.648917   -1.379388
             pre1 |   -.013885   .3134971    -0.04   0.965    -.6283279     .600558
             pre2 |   .4250014   .2998346     1.42   0.156    -.1626637    1.012667
             pre3 |   .1083129   .2766999     0.39   0.695     -.434009    .6506348
             pre4 |  -.0520192   .2161892    -0.24   0.810    -.4757422    .3717039
        ln_ew_ges |  -.9829459   1.217608    -0.81   0.420    -3.369413    1.403521
         ew_biodt |   .3684063   .0316825    11.63   0.000     .3063098    .4305029
        ew_dtmihi |   .0679659   .0602154     1.13   0.259    -.0500541    .1859859
         ew_ledig |    .212986   .0734978     2.90   0.004     .0689329     .357039
       ew_married |   .3566134   .0734084     4.86   0.000     .2127356    .5004913
        wb_anteil |  -.2364891   .0220211   -10.74   0.000    -.2796498   -.1933285
          wb_ausl |   .0330947    .017156     1.93   0.054    -.0005305    .0667199
         wb_18t24 |  -.0634038   .0350962    -1.81   0.071     -.132191    .0053835
         wb_25t34 |  -.0119971   .0196674    -0.61   0.542    -.0505444    .0265503
         wb_35t44 |  -.0228091   .0263721    -0.86   0.387    -.0744975    .0288792
         wb_45t59 |   .0323736    .026005     1.24   0.213    -.0185952    .0833425
          avg_dur |  -.0289143     .02399    -1.21   0.228    -.0759339    .0181053
          hh_kids |   .0247097   .0456163     0.54   0.588    -.0646965    .1141159
mpreis_flats_rent |   .0809056   .0246687     3.28   0.001     .0325559    .1292553
-----------------------------------------------------------------------------------

 ( 1)  - tau0 + tau1 = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   -.169346   .2475916    -0.68   0.494    -.6546166    .3159245
------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -2.021205   .2987583  -2.606771  -1.435639    3431972     258230 
    Effect_1 | -2.298784   .3869354  -3.057177   -1.54039    2591231     143977 
    Effect_2 | -2.062697   .4472415  -2.939291  -1.186104    1860509   111437.6 
     Average | -2.108014   .3164068  -2.728171  -1.487856    7883712   513644.6 
   Placebo_1 | -.4084362   .1558995  -.7139992  -.1028732    3431972     258230 
   Placebo_2 |  .4019998   .2056429  -.0010603     .80506    2547567   250895.3 
   Placebo_3 |  .0390803   .1713225  -.2967118   .3748723    2547567   250895.3 
   Placebo_4 | -.1041872   .2545869  -.6031775   .3948031    1799946     100750 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  28,    509) =      17.97
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9652
                                                  Adj R-squared   =     0.9595
                                                  Within R-sq.    =     0.1809
Number of clusters (sb_new)  =        510         Root MSE        =     1.8516

                                    (Std. err. adjusted for 510 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.5517929   .4446331    -1.24   0.215    -1.425335    .3217491
          F6event |  -.2134358   .3870674    -0.55   0.582    -.9738822    .5470106
          F5event |  -.0427538   .3229096    -0.13   0.895    -.6771534    .5916458
          F4event |  -.0439541   .2407803    -0.18   0.855    -.5169996    .4290913
          F3event |   .1511852   .2599414     0.58   0.561    -.3595049    .6618754
          F2event |   .4480818   .1760632     2.55   0.011     .1021817    .7939819
          L0event |  -1.952144   .2896849    -6.74   0.000    -2.521269   -1.383018
          L1event |  -1.974757   .3106431    -6.36   0.000    -2.585057   -1.364456
          L2event |   -1.66967   .3322171    -5.03   0.000    -2.322355   -1.016984
          L3event |  -.9839997   .3396127    -2.90   0.004    -1.651215   -.3167845
          L4event |  -.8667189    .759207    -1.14   0.254    -2.358284    .6248463
          L5event |   -.130914   .7801883    -0.17   0.867      -1.6637    1.401872
          L6event |  -1.480285   .5016082    -2.95   0.003    -2.465762    -.494808
          L7event |   1.242252    1.79792     0.69   0.490    -2.290005    4.774509
          F1event |          0  (omitted)
        ln_ew_ges |  -1.006495   1.030211    -0.98   0.329    -3.030484    1.017494
         ew_biodt |   .3545531   .0305649    11.60   0.000     .2945041    .4146021
        ew_dtmihi |   .0447468   .0545528     0.82   0.412    -.0624296    .1519231
         ew_ledig |   .2064001   .0616595     3.35   0.001     .0852617    .3275386
       ew_married |   .3654755   .0624213     5.85   0.000     .2428405    .4881106
        wb_anteil |  -.2309445   .0221964   -10.40   0.000    -.2745524   -.1873366
          wb_ausl |   .0296728   .0165626     1.79   0.074    -.0028668    .0622123
         wb_18t24 |  -.0531407   .0324855    -1.64   0.102     -.116963    .0106815
         wb_25t34 |  -.0213724   .0177408    -1.20   0.229    -.0562267    .0134818
         wb_35t44 |  -.0109729   .0233747    -0.47   0.639    -.0568956    .0349498
         wb_45t59 |  -.0019266   .0230673    -0.08   0.933    -.0472455    .0433923
          avg_dur |  -.0122583   .0221839    -0.55   0.581    -.0558417     .031325
          hh_kids |   .0081451   .0434518     0.19   0.851    -.0772219    .0935121
mpreis_flats_rent |   .0553077   .0209475     2.64   0.009     .0141534    .0964619
            _cons |   11.41114   9.472409     1.20   0.229    -7.198688    30.02097
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       510         510           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0226132   .2216994    -0.10   0.919    -.4581717    .4129453
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 5,757,292.8377406)
(obs=3,704)

IW estimates for dynamic effects                        Number of obs =  3,904
Absorbing 2 HDFE groups                                 F(49, 509)    = 206.04
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.9660
                                                        Adj R-squared = 0.9603
                                                        Root MSE      = 1.8341
                               (Std. err. adjusted for 510 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |  -.1942906   1.210544    -0.16   0.873    -2.572569    2.183987
     F5event |   1.031907   .4679965     2.20   0.028     .1124643     1.95135
     F4event |   -.188577   .2634035    -0.72   0.474    -.7060689    .3289148
     F3event |   .0752979   .2851967     0.26   0.792    -.4850097    .6356056
     F2event |   .5041111    .192412     2.62   0.009     .1260917    .8821305
     L0event |  -2.409904   .3331143    -7.23   0.000    -3.064352   -1.755456
     L1event |  -2.233689   .3411913    -6.55   0.000    -2.904006   -1.563373
     L2event |  -1.975319   .3582269    -5.51   0.000    -2.679104   -1.271533
     L3event |  -1.411384   .3390939    -4.16   0.000     -2.07758    -.745188
     L4event |  -1.367188   1.481163    -0.92   0.356    -4.277133    1.542757
     L5event |   -.225078   1.367552    -0.16   0.869     -2.91182    2.461664
     L6event |  -2.896669   1.459712    -1.98   0.048     -5.76447   -.0288674
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1762147   .2695788     0.65   0.513    -.3521501    .7045794
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 3,871
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |     .12189    .071843     1.70   0.090    -.0189198    .2626997
    Post_avg |  -.3664795   .3167902    -1.16   0.247    -.9873769     .254418
         Tm6 |   .2663805   .2463162     1.08   0.279    -.2163903    .7491512
         Tm5 |   .5103152   .3744892     1.36   0.173    -.2236703    1.244301
         Tm4 |  -.1367943   .2591218    -0.53   0.598    -.6446638    .3710751
         Tm3 |   .1265391   .1847698     0.68   0.493    -.2356031    .4886813
         Tm2 |   .3878369   .2196662     1.77   0.077     -.042701    .8183748
         Tm1 |  -.4229375   .2071945    -2.04   0.041    -.8290313   -.0168438
         Tp0 |  -2.120293   .3069411    -6.91   0.000    -2.721887     -1.5187
         Tp1 |  -2.371612   .3445081    -6.88   0.000    -3.046836   -1.696389
         Tp2 |  -1.991926   .4029366    -4.94   0.000    -2.781668   -1.202185
         Tp3 |  -1.442895   .3760769    -3.84   0.000    -2.179992   -.7057979
         Tp4 |   2.492275    .913162     2.73   0.006     .7025101    4.282039
         Tp5 |   3.235576   .6000669     5.39   0.000     2.059466    4.411685
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |     .12189    .071843     1.70   0.090    -.0189198    .2626997
    Post_avg |  -.3664795   .3167902    -1.16   0.247    -.9873769     .254418
         Tm6 |   .2663805   .2463162     1.08   0.279    -.2163903    .7491512
         Tm5 |   .5103152   .3744892     1.36   0.173    -.2236703    1.244301
         Tm4 |  -.1367943   .2591218    -0.53   0.598    -.6446638    .3710751
         Tm3 |   .1265391   .1847698     0.68   0.493    -.2356031    .4886813
         Tm2 |   .3878369   .2196662     1.77   0.077     -.042701    .8183748
         Tm1 |  -.4229375   .2071945    -2.04   0.041    -.8290313   -.0168438
         Tp0 |  -2.120293   .3069411    -6.91   0.000    -2.721887     -1.5187
         Tp1 |  -2.371612   .3445081    -6.88   0.000    -3.046836   -1.696389
         Tp2 |  -1.991926   .4029366    -4.94   0.000    -2.781668   -1.202185
         Tp3 |  -1.442895   .3760769    -3.84   0.000    -2.179992   -.7057979
         Tp4 |   2.492275    .913162     2.73   0.006     .7025101    4.282039
         Tp5 |   3.235576   .6000669     5.39   0.000     2.059466    4.411685
------------------------------------------------------------------------------
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 3,816
-----------------------------------------------------------------------------------
  turnout_pos_req | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |    1.39201   .2290762     6.08   0.000     .9430293    1.840991
             tau1 |   2.011231   .2813425     7.15   0.000      1.45981    2.562652
             tau2 |   2.034634   .3595369     5.66   0.000     1.329955    2.739314
             pre1 |   .0755689   .2908049     0.26   0.795    -.4943983    .6455361
             pre2 |  -.2513149   .3084515    -0.81   0.415    -.8558687    .3532389
             pre3 |  -.2240025   .3156076    -0.71   0.478     -.842582    .3945771
             pre4 |   .0156015   .2140471     0.07   0.942    -.4039231     .435126
        ln_ew_ges |   2.285465   1.697129     1.35   0.178    -1.040847    5.611776
         ew_biodt |    .450219   .0356478    12.63   0.000     .3803506    .5200873
        ew_dtmihi |  -.1941968   .0729105    -2.66   0.008    -.3370988   -.0512948
         ew_ledig |   .1796407   .0906758     1.98   0.048     .0019194    .3573619
       ew_married |   .2161219   .0916101     2.36   0.018     .0365693    .3956745
        wb_anteil |   -.305469   .0318622    -9.59   0.000    -.3679177   -.2430203
          wb_ausl |  -.0535965   .0174277    -3.08   0.002    -.0877542   -.0194389
         wb_18t24 |  -.0205948   .0343186    -0.60   0.548    -.0878581    .0466684
         wb_25t34 |   .0195648    .019629     1.00   0.319    -.0189073    .0580369
         wb_35t44 |   .0067605   .0274652     0.25   0.806    -.0470703    .0605913
         wb_45t59 |  -.0271999   .0251026    -1.08   0.279    -.0764001    .0220003
          avg_dur |   .0588133   .0282513     2.08   0.037     .0034417    .1141848
          hh_kids |  -.0752248   .0539964    -1.39   0.164    -.1810558    .0306063
mpreis_flats_rent |   .0291598   .0279226     1.04   0.296    -.0255674     .083887
-----------------------------------------------------------------------------------

 ( 1)  - tau0 + tau1 = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6192209   .2239598     2.76   0.006     .1802678    1.058174
------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  1.229184   .2681223   .7036641   1.754704    3431972     258230 
    Effect_1 |  2.036092   .3293427   1.390581   2.681604    2591231     143977 
    Effect_2 |  2.057263   .4161769   1.241556    2.87297    1860509   111437.6 
     Average |   1.63502   .2765843   1.092915   2.177125    7883712   513644.6 
   Placebo_1 |  .2866413   .1772541  -.0607767   .6340593    3431972     258230 
   Placebo_2 | -.0526836   .2389875  -.5210991   .4157319    2547567   250895.3 
   Placebo_3 | -.0732579   .1902023  -.4460544   .2995386    2547567   250895.3 
   Placebo_4 |  .4663864   .2777325  -.0779692   1.010742    1799946     100750 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  28,    509) =      14.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9469
                                                  Adj R-squared   =     0.9383
                                                  Within R-sq.    =     0.2176
Number of clusters (sb_new)  =        510         Root MSE        =     1.9280

                                    (Std. err. adjusted for 510 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .8176466   .2904544     2.82   0.005     .2470095    1.388284
          F6event |   .5279281   .2781862     1.90   0.058    -.0186064    1.074463
          F5event |  -.0386582   .3380611    -0.11   0.909     -.702825    .6255086
          F4event |  -.0131224   .2092516    -0.06   0.950    -.4242255    .3979807
          F3event |  -.3095771   .2424885    -1.28   0.202    -.7859787    .1668244
          F2event |  -.3093433   .1739048    -1.78   0.076    -.6510028    .0323161
          L0event |   1.161739   .2844175     4.08   0.000     .6029623    1.720516
          L1event |   1.794512   .2976008     6.03   0.000     1.209835    2.379189
          L2event |   1.700366   .3525061     4.82   0.000      1.00782    2.392912
          L3event |   .9899427   .3424071     2.89   0.004     .3172374    1.662648
          L4event |   3.264292   .6086248     5.36   0.000     2.068566    4.460018
          L5event |   1.517553   .7096975     2.14   0.033     .1232565     2.91185
          L6event |   1.740861   .3848115     4.52   0.000     .9848462    2.496875
          L7event |   .7566665   1.784265     0.42   0.672    -2.748765    4.262098
          F1event |          0  (omitted)
        ln_ew_ges |   2.780298   1.511859     1.84   0.066    -.1899541    5.750549
         ew_biodt |    .439518   .0338407    12.99   0.000     .3730333    .5060026
        ew_dtmihi |  -.2300929   .0669396    -3.44   0.001    -.3616047    -.098581
         ew_ledig |   .1675381   .0892449     1.88   0.061    -.0077956    .3428717
       ew_married |   .2149374    .089042     2.41   0.016     .0400023    .3898725
        wb_anteil |  -.2983277   .0304929    -9.78   0.000    -.3582352   -.2384203
          wb_ausl |  -.0637327   .0162188    -3.93   0.000    -.0955966   -.0318687
         wb_18t24 |  -.0178223   .0313877    -0.57   0.570    -.0794876    .0438431
         wb_25t34 |   .0148675   .0182921     0.81   0.417    -.0210698    .0508047
         wb_35t44 |  -.0027783   .0243777    -0.11   0.909    -.0506715    .0451149
         wb_45t59 |  -.0106945   .0223201    -0.48   0.632    -.0545454    .0331565
          avg_dur |   .0444922     .02491     1.79   0.075    -.0044469    .0934313
          hh_kids |  -.0594489   .0477872    -1.24   0.214    -.1533333    .0344355
mpreis_flats_rent |   .0431176   .0239875     1.80   0.073     -.004009    .0902442
            _cons |   -13.0452   13.05928    -1.00   0.318    -38.70193    12.61154
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       510         510           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6327727   .2042311     3.10   0.002     .2315331    1.034012
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 5,757,292.8377406)
(obs=3,704)

IW estimates for dynamic effects                       Number of obs =   3,904
Absorbing 2 HDFE groups                                F(49, 509)    = 1008.58
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.9488
                                                       Adj R-squared =  0.9401
                                                       Root MSE      =  1.8999
                               (Std. err. adjusted for 510 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |   .4359155   .7349305     0.59   0.553    -1.007955    1.879786
     F5event |    -.54397   .4125676    -1.32   0.188    -1.354515     .266575
     F4event |    .108501   .1992493     0.54   0.586    -.2829513    .4999534
     F3event |  -.2311323   .2710786    -0.85   0.394    -.7637029    .3014384
     F2event |  -.3460469   .1943436    -1.78   0.076    -.7278612    .0357675
     L0event |   1.646203   .3144823     5.23   0.000     1.028359    2.264046
     L1event |   1.892273   .3265628     5.79   0.000     1.250696     2.53385
     L2event |   1.803086   .3663582     4.92   0.000     1.083325    2.522846
     L3event |   1.179329   .3364835     3.50   0.000     .5182619    1.840397
     L4event |   2.354721   1.243822     1.89   0.059    -.0889363    4.798378
     L5event |   .3700294   1.372412     0.27   0.788     -2.32626    3.066318
     L6event |   1.085184   1.620844     0.67   0.503    -2.099183    4.269552
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2460709   .2630517     0.94   0.350     -.269501    .7616427
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 3,871
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |  -.0749208   .0496855    -1.51   0.132    -.1723026     .022461
    Post_avg |   1.185354   .7355679     1.61   0.107    -.2563329     2.62704
         Tm6 |   .1055882   .2087895     0.51   0.613    -.3036316     .514808
         Tm5 |  -1.147151   .4368805    -2.63   0.009    -2.003421   -.2908809
         Tm4 |   .4684448   .3109965     1.51   0.132    -.1410972    1.077987
         Tm3 |  -.0324984   .1935353    -0.17   0.867    -.4118206    .3468239
         Tm2 |  -.1386357   .2617291    -0.53   0.596    -.6516153     .374344
         Tm1 |   .2947274   .1901434     1.55   0.121    -.0779468    .6674015
         Tp0 |    1.18602   .3068855     3.86   0.000     .5845356    1.787505
         Tp1 |   2.027702   .3536238     5.73   0.000     1.334612    2.720792
         Tp2 |   2.091018   .4488456     4.66   0.000     1.211296    2.970739
         Tp3 |   1.341355   .4972542     2.70   0.007     .3667545    2.315955
         Tp4 |   1.887491   1.662754     1.14   0.256    -1.371447     5.14643
         Tp5 |  -1.421463   2.381379    -0.60   0.551    -6.088881    3.245954
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |  -.0749208   .0496855    -1.51   0.132    -.1723026     .022461
    Post_avg |   1.185354   .7355679     1.61   0.107    -.2563329     2.62704
         Tm6 |   .1055882   .2087895     0.51   0.613    -.3036316     .514808
         Tm5 |  -1.147151   .4368805    -2.63   0.009    -2.003421   -.2908809
         Tm4 |   .4684448   .3109965     1.51   0.132    -.1410972    1.077987
         Tm3 |  -.0324984   .1935353    -0.17   0.867    -.4118206    .3468239
         Tm2 |  -.1386357   .2617291    -0.53   0.596    -.6516153     .374344
         Tm1 |   .2947274   .1901434     1.55   0.121    -.0779468    .6674015
         Tp0 |    1.18602   .3068855     3.86   0.000     .5845356    1.787505
         Tp1 |   2.027702   .3536238     5.73   0.000     1.334612    2.720792
         Tp2 |   2.091018   .4488456     4.66   0.000     1.211296    2.970739
         Tp3 |   1.341355   .4972542     2.70   0.007     .3667545    2.315955
         Tp4 |   1.887491   1.662754     1.14   0.256    -1.371447     5.14643
         Tp5 |  -1.421463   2.381379    -0.60   0.551    -6.088881    3.245954
------------------------------------------------------------------------------
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 3,816
-----------------------------------------------------------------------------------
  turnout_tot_req | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |  -.7516505   .1798216    -4.18   0.000    -1.104094   -.3992067
             tau1 |  -.3017756   .2616539    -1.15   0.249    -.8146079    .2110567
             tau2 |   .0204815   .3108424     0.07   0.947    -.5887584    .6297213
             pre1 |   .0616836    .278763     0.22   0.825    -.4846819    .6080491
             pre2 |   .1736869   .2471879     0.70   0.482    -.3107924    .6581663
             pre3 |  -.1156897   .2268345    -0.51   0.610    -.5602771    .3288977
             pre4 |  -.0364174   .2219287    -0.16   0.870    -.4713896    .3985548
        ln_ew_ges |   1.302519   1.206281     1.08   0.280    -1.061748    3.666787
         ew_biodt |   .8186253   .0375164    21.82   0.000     .7450945     .892156
        ew_dtmihi |  -.1262309   .0605973    -2.08   0.037    -.2449994   -.0074623
         ew_ledig |   .3926269   .0764854     5.13   0.000     .2427183    .5425354
       ew_married |   .5727356   .0775209     7.39   0.000     .4207973    .7246738
        wb_anteil |  -.5419581    .033205   -16.32   0.000    -.6070386   -.4768775
          wb_ausl |  -.0205018   .0160653    -1.28   0.202    -.0519893    .0109856
         wb_18t24 |  -.0839986     .03292    -2.55   0.011    -.1485205   -.0194767
         wb_25t34 |   .0075677   .0177234     0.43   0.669    -.0271695     .042305
         wb_35t44 |  -.0160486   .0243207    -0.66   0.509    -.0637163    .0316191
         wb_45t59 |   .0051737   .0236196     0.22   0.827    -.0411198    .0514673
          avg_dur |    .029899   .0270411     1.11   0.269    -.0231007    .0828987
          hh_kids |  -.0505152   .0415959    -1.21   0.225    -.1320416    .0310113
mpreis_flats_rent |   .1100654   .0262403     4.19   0.000     .0586353    .1614955
-----------------------------------------------------------------------------------

 ( 1)  - tau0 + tau1 = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4498749   .2259755     1.99   0.047     .0069712    .8927786
------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.7920205    .207514  -1.198748   -.385293    3431972     258230 
    Effect_1 | -.2626898   .3273836  -.9043616    .378982    2591231     143977 
    Effect_2 | -.0054332   .3902825  -.7703869   .7595204    1860509   111437.6 
     Average | -.4729928   .2400924  -.9435739  -.0024116    7883712   513644.6 
   Placebo_1 | -.1217955   .1899505  -.4940984   .2505075    3431972     258230 
   Placebo_2 |  .3493165   .1753579   .0056151   .6930179    2547567   250895.3 
   Placebo_3 | -.0341779   .1669319  -.3613644   .2930086    2547567   250895.3 
   Placebo_4 |  .3621994   .3029694  -.2316206   .9560193    1799946     100750 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  28,    509) =      40.07
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9874
                                                  Adj R-squared   =     0.9853
                                                  Within R-sq.    =     0.4402
Number of clusters (sb_new)  =        510         Root MSE        =     1.8035

                                    (Std. err. adjusted for 510 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .2658539   .3845251     0.69   0.490    -.4895977    1.021305
          F6event |   .3144918   .3315756     0.95   0.343    -.3369335     .965917
          F5event |  -.0814116   .3106282    -0.26   0.793    -.6916829    .5288596
          F4event |   -.057076   .2078692    -0.27   0.784    -.4654632    .3513113
          F3event |  -.1583918   .2067165    -0.77   0.444    -.5645144    .2477308
          F2event |   .1387391   .1882192     0.74   0.461    -.2310429    .5085212
          L0event |  -.7904037   .2066663    -3.82   0.000    -1.196428   -.3843797
          L1event |   -.180244   .2653966    -0.68   0.497    -.7016516    .3411636
          L2event |   .0306966   .3063697     0.10   0.920    -.5712082    .6326015
          L3event |   .0059435   .3308686     0.02   0.986    -.6440927    .6559797
          L4event |   2.397572   .8230915     2.91   0.004     .7804976    4.014647
          L5event |   1.386639   .5053797     2.74   0.006     .3937519    2.379526
          L6event |   .2605729   .3791532     0.69   0.492     -.484325    1.005471
          L7event |   1.998919   .3397976     5.88   0.000     1.331341    2.666497
          F1event |          0  (omitted)
        ln_ew_ges |   1.773802   1.119841     1.58   0.114    -.4262778    3.973882
         ew_biodt |   .7940711    .035942    22.09   0.000     .7234581     .864684
        ew_dtmihi |   -.185346   .0583807    -3.17   0.002    -.3000428   -.0706493
         ew_ledig |   .3739384   .0803924     4.65   0.000     .2159966    .5318802
       ew_married |    .580413   .0797005     7.28   0.000     .4238307    .7369954
        wb_anteil |  -.5292722   .0312817   -16.92   0.000    -.5907294    -.467815
          wb_ausl |  -.0340599    .015259    -2.23   0.026    -.0640384   -.0040815
         wb_18t24 |   -.070963   .0305531    -2.32   0.021    -.1309888   -.0109372
         wb_25t34 |   -.006505   .0161916    -0.40   0.688    -.0383156    .0253056
         wb_35t44 |  -.0137512   .0216597    -0.63   0.526    -.0563047    .0288023
         wb_45t59 |  -.0126211   .0206894    -0.61   0.542    -.0532683    .0280262
          avg_dur |   .0322338   .0238682     1.35   0.177    -.0146584    .0791261
          hh_kids |  -.0513038   .0387113    -1.33   0.186    -.1273573    .0247497
mpreis_flats_rent |   .0984253   .0224493     4.38   0.000     .0543205    .1425301
            _cons |  -1.634064   10.83889    -0.15   0.880    -22.92853     19.6604
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       510         510           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6101597    .205551     2.97   0.003     .2063269    1.013992
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 5,757,292.8377406)
(obs=3,704)

IW estimates for dynamic effects                        Number of obs =  3,904
Absorbing 2 HDFE groups                                 F(49, 509)    = 608.10
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.9875
                                                        Adj R-squared = 0.9854
                                                        Root MSE      = 1.8006
                               (Std. err. adjusted for 510 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |   .2416256   .9552741     0.25   0.800     -1.63514    2.118391
     F5event |   .4879386   .3873156     1.26   0.208    -.2729954    1.248873
     F4event |  -.0800753   .2328798    -0.34   0.731    -.5375993    .3774486
     F3event |  -.1558342   .2134171    -0.73   0.466     -.575121    .2634525
     F2event |    .158065   .2185555     0.72   0.470    -.2713168    .5874469
     L0event |  -.7637006    .229175    -3.33   0.001    -1.213946   -.3134553
     L1event |  -.3414149   .2696664    -1.27   0.206     -.871211    .1883812
     L2event |  -.1722328   .3107389    -0.55   0.580    -.7827216    .4382559
     L3event |  -.2320544   .3551717    -0.65   0.514    -.9298373    .4657285
     L4event |   .9875325   .9333071     1.06   0.291    -.8460758    2.821141
     L5event |   .1449515   .4008591     0.36   0.718    -.6425904    .9324935
     L6event |  -1.811487   .2277265    -7.95   0.000    -2.258887   -1.364087
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4222857   .2100421     2.01   0.044     .0106107    .8339607
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 3,871
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0469691    .061053     0.77   0.442    -.0726926    .1666307
    Post_avg |   .8188747    .611261     1.34   0.180    -.3791748    2.016924
         Tm6 |   .3719676   .2238456     1.66   0.097    -.0667617    .8106968
         Tm5 |  -.6368351   .4384069    -1.45   0.146    -1.496097    .2224267
         Tm4 |   .3316506   .4135691     0.80   0.423    -.4789299    1.142231
         Tm3 |   .0940402    .192956     0.49   0.626    -.2841465     .472227
         Tm2 |   .2492018   .2312597     1.08   0.281    -.2040589    .7024626
         Tm1 |  -.1282108   .2645112    -0.48   0.628    -.6466433    .3902216
         Tp0 |  -.9342725   .2418129    -3.86   0.000    -1.408217    -.460328
         Tp1 |   -.343909   .3569443    -0.96   0.335    -1.043507    .3556889
         Tp2 |   .0990916   .4498368     0.22   0.826    -.7825724    .9807555
         Tp3 |  -.1015398   .5886503    -0.17   0.863    -1.255273    1.052193
         Tp4 |   4.379764   1.272188     3.44   0.001     1.886321    6.873207
         Tp5 |   1.814114   1.943674     0.93   0.351    -1.995416    5.623645
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0469691    .061053     0.77   0.442    -.0726926    .1666307
    Post_avg |   .8188747    .611261     1.34   0.180    -.3791748    2.016924
         Tm6 |   .3719676   .2238456     1.66   0.097    -.0667617    .8106968
         Tm5 |  -.6368351   .4384069    -1.45   0.146    -1.496097    .2224267
         Tm4 |   .3316506   .4135691     0.80   0.423    -.4789299    1.142231
         Tm3 |   .0940402    .192956     0.49   0.626    -.2841465     .472227
         Tm2 |   .2492018   .2312597     1.08   0.281    -.2040589    .7024626
         Tm1 |  -.1282108   .2645112    -0.48   0.628    -.6466433    .3902216
         Tp0 |  -.9342725   .2418129    -3.86   0.000    -1.408217    -.460328
         Tp1 |   -.343909   .3569443    -0.96   0.335    -1.043507    .3556889
         Tp2 |   .0990916   .4498368     0.22   0.826    -.7825724    .9807555
         Tp3 |  -.1015398   .5886503    -0.17   0.863    -1.255273    1.052193
         Tp4 |   4.379764   1.272188     3.44   0.001     1.886321    6.873207
         Tp5 |   1.814114   1.943674     0.93   0.351    -1.995416    5.623645
------------------------------------------------------------------------------

.         * PLOT: FIGURE D11. Event Study Results Restricted to Units with Increased Distance
.         grc1leg  turnout_urne turnout_pos_req turnout_tot_req , xcommon  col(2) iscale(.65) imar
> gins(small) pos(12)

.         gr_edit .style.editstyle declared_ysize(4) editcopy

.         gr_edit .legend.DragBy -60 40

.         gr_edit .legend.Edit , style(cols(1)) style(rows(0)) keepstyles

.         gr_edit .legend.title.text.Arrpush "Estimator:"

.         gr_edit .legend.title.style.editstyle size(small) editcopy

.         gr_edit .legend.title.DragBy 0 -15

.         graph export "$figures/Figure_D11_ES_novel_ctr_wgt_dist_increase.pdf", replace  
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D11_ES
    > _novel_ctr_wgt_dist_increase.pdf saved as PDF format

. 
. * Save: first part of Table E5 (appended and exported below)
.         frame testsUp: drop p t 

.         frame testsUp: reshape wide delta, i(estimator) j(turnout) string
(j = turnout_pos_req turnout_tot_req turnout_urne)

Data                               Long   ->   Wide
-----------------------------------------------------------------------------
Number of observations               15   ->   5           
Number of variables                   3   ->   4           
j variable (3 values)           turnout   ->   (dropped)
xij variables:
                                  delta   ->   deltaturnout_pos_req deltaturnout_tot_req deltaturn
> out_urne
-----------------------------------------------------------------------------

.         frame testsUp: rename delta* *

.         frame testsUp: list

     +-------------------------------------------------+
     |  estimator   turn~s_req   turn~t_req   turnou~e |
     |-------------------------------------------------|
  1. | BJS (2021)      0.619**       0.450*     -0.169 |
  2. |  CS (2021)      0.842**       0.590*     -0.251 |
  3. |  SA (2020)        0.246       0.422*      0.176 |
  4. |   TWFE-OLS      0.633**      0.610**     -0.023 |
  5. | dCDH (2020      0.807**       0.529*     -0.278 |
     +-------------------------------------------------+

.         // save and append below        
.         frame testsUp: save "$tmp/ES_test_tau1_tau2_only_dist_up.dta", replace                  
(file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/tmp/ES_test_tau1_tau2_only_dist
    > _up.dta not found)
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/tmp/ES_test_tau1_tau2_only_dist
    > _up.dta saved

.         
.         
. ********************************************************************************
.  // Test of Mechanism: ES conditional on log distance, novel DID (Fiure D12) //
. ********************************************************************************
. 
. // create frame to post test results to
. cap frame drop testsAbs

.         frame create testsAbs str10 estimator str20 turnout str20 delta double(p t)

. 
. foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {     
  2.         
.         * BJS (2021) (weights possible)
.         did_imputation `v' sb_new wahl_id Ei if smpl_trim==1 [aw=wahlber_gesamt], autosample pre
> trends(4) horizons(0/2) cluster(sb_new) maxit(3000) controls($ctr ln_street_dist)
  3.         estimates store bjs 
  4.         
.         // test: t_1 - t_0
.         lincom tau1-tau0        
  5.         local diff = "`:di %8.3f `r(estimate)''"
  6.         stars `r(p)',   stat(p)
  7.         local delta "`diff'`r(stars)'"
  8.         frame post testsAbs ("BJS (2021)") ("`v'") ("`delta'") (`r(p)') (.)
  9. 
.         * Estimation with did_multiplegt of de Chaisemartin and D'Haultfoeuille (2020) (NO weigh
> ts allowed)
.         did_multiplegt `v' sb_new wahl_id D  if smpl_trim==1, robust_dynamic dynamic(2) placebo(
> 4) firstdiff_placebo breps(100) seed(100) cluster(sb_new)  controls($ctr ln_street_dist) covaria
> nces
 10. 
.         matrix dcdh_b = e(estimates) // storing the estimates
 11.         matrix dcdh_v = e(variances)
 12.         
.         // test: t_1 - t_0      
.         local  diff = e(effect_1)-e(effect_0) //  t_1 - t_0
 13.         local diff = "`:di %8.3f `diff''"       
 14.         local  se = sqrt(e(se_effect_1)^2+e(se_effect_0)^2-2*e(cov_effects_0_1)) // SE
 15.         local  tstat = `diff'/`se'
 16.         stars `tstat',  stat(t)
 17.         local delta "`diff'`r(stars)'"  
 18.         frame post testsAbs ("dCDH (2020)") ("`v'") ("`delta'") (.) (`r(t)')
 19.         
.         * TWFE OLS (weights possible)
.         cap drop L* F*
 20.         forvalues l = 7(-1)1 {
 21.                 gen F`l'event = K==-`l'
 22.         }       
 23.         forvalues l = 0/7 {
 24.                 gen L`l'event = K==`l'
 25.         }       
 26.                 reghdfe `v' F7event-F2event L0event-L7event F1event $ctr  ln_street_dist [aw=
> wahlber_gesamt] if smpl_trim ==1, absorb(i.wahl_id i.sb_new) cluster(sb_new)
 27.         estimates store ols     
 28.         
.         // test: t_1 - t_0
.         lincom L1event-L0event
 29.         local diff = "`:di %8.3f `r(estimate)''"
 30.         stars `r(p)',   stat(p)
 31.         local delta "`diff'`r(stars)'"
 32.         frame post testsAbs ("TWFE-OLS") ("`v'") ("`delta'") (`r(p)') (.)       
 33.         
.         * Estimation with eventstudyinteract of Sun and Abraham (2020) (weights allowed)
.         cap drop L* F*
 34.         cap drop lastcohort
 35.         sum Ei
 36.         gen lastcohort = Ei==r(max) // dummy for the latest- or never-treated cohort
 37.         forvalues l = 7(-1)1 {
 38.                 gen F`l'event = K==-`l'
 39.         }       
 40.         forvalues l = 0/7 {
 41.                 gen L`l'event = K==`l'
 42.         }
 43.         order F1event, last
 44.         eventstudyinteract `v' F7event-L7event F1event  if smpl_trim==1 [aw=wahlber_gesamt], 
> vce(cluster sb_new) absorb(sb_new wahl_id) cohort(Ei) control_cohort(lastcohort) covariates($ctr
>   ln_street_dist)
 45.         
.         matrix sa_b = e(b_iw) // storing the estimates
 46.         matrix sa_v = e(V_iw)
 47.         
.         // test: t_1 - t_0
.          matrix b = e(b_iw)
 48.      matrix V = e(V_iw)
 49.      ereturn post b V
 50.         lincom L1event-L0event
 51.         local diff = "`:di %8.3f `r(estimate)''"
 52.         stars `r(p)',   stat(p)
 53.         local delta "`diff'`r(stars)'"
 54.         frame post testsAbs ("SA (2020)") ("`v'") ("`delta'") (`r(p)') (.)
 55.         
. 
.         * Estimation with csdid of Callaway and Sant'Anna (2020) (weights allowed ); Version. v1
> .72
.         cap drop gvar
 56.         gen gvar = cond(Ei==., 0, Ei)                                   // group variable as 
> required for the csdid command
 57.         csdid `v' $ctr  ln_street_dist  if smpl_trim==1 [w=wahlber_gesamt] , ivar(sb_new) tim
> e(wahl_id) gvar(gvar) notyet agg(event) method(dripw)
 58.         estat event, estore(cs)                                                 // this produ
> ces and stores the estimates at the same time                      
 59.                                 
.         // test: t_1 - t_0
.         local diff      = e(b)[1,10]-e(b)[1,9] 
 60.         local diff = "`:di %8.3f `diff''"
 61.         local se        = sqrt(e(V)[10,10]+e(V)[9,9]-2*e(V)[10,9]) 
 62.         local tstat = `diff'/`se'
 63.         stars `tstat',  stat(t)
 64.         local delta "`diff'`r(stars)'"
 65.         frame post testsAbs ("CS (2021)") ("`v'") ("`delta'") (.) (`r(t)')      
 66.                                 
. *** Combine all plots using the stored estimates
. 
.         event_plot  bjs dcdh_b#dcdh_v cs ols sa_b#sa_v , ///
>         stub_lag(tau# Effect_# Tp# L#event L#event ) stub_lead(pre# Placebo_# Tm# F#event F#even
> t ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.325(0.13)0.325) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(title("`:var lab `v''", nobox span bexpand justification(left) size(medium)) /
> //
>                 xtitle("Election since reassignment", size(medsmall)) ///
>                 ytitle("Voter turnout in %""(estimates)") xlabel(-4(1)2) ///
>                 legend(pos(6) order(1 "Borusyak et al. (2022)" 3 "de Chaisemartin & D'Haultfoeui
> lle (2020)" 5 "Callaway & Sant'Anna (2021)" 7 "TWFE OLS" 9 "Sun & Abraham (2021)") col(2) region
> (style(none)) size(vsmall)) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid))  y
> label(, angle(horizontal)) ///
>                 name(`v',replace) ) ///
>         lag_opt1(msymbol(+)     msize(3pt) color(black))                lag_ci_opt1(color(black)
> ) ///
>         lag_opt2(msymbol(O)     msize(3pt) color(midblue))              lag_ci_opt2(color(midblu
> e)) ///
>         lag_opt3(msymbol(Dh)    msize(3pt) color(dkorange))     lag_ci_opt3(color(dkorange)) ///
>         lag_opt4(msymbol(Th)    msize(3pt) color(teal))                 lag_ci_opt4(color(teal))
>  ///
>         lag_opt5(msymbol(Sh)    msize(3pt) color(cranberry))    lag_ci_opt5(color(cranberry))
 67. }
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 4,504
-----------------------------------------------------------------------------------
     turnout_urne | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |  -.7538639   .1896536    -3.97   0.000    -1.125578   -.3821497
             tau1 |  -.7882326   .2142979    -3.68   0.000    -1.208249   -.3682165
             tau2 |   -.634152    .239451    -2.65   0.008    -1.103467   -.1648366
             pre1 |   -.029603   .2567344    -0.12   0.908    -.5327931    .4735871
             pre2 |   .1037293   .2316516     0.45   0.654    -.3502996    .5577582
             pre3 |  -.1582968   .2177318    -0.73   0.467    -.5850432    .2684497
             pre4 |  -.1808816   .1832066    -0.99   0.323      -.53996    .1781968
        ln_ew_ges |  -1.368219    1.10886    -1.23   0.217    -3.541544     .805106
         ew_biodt |   .3774911   .0291061    12.97   0.000     .3204442     .434538
        ew_dtmihi |   .0575552   .0590386     0.97   0.330    -.0581583    .1732687
         ew_ledig |   .2081255   .0672875     3.09   0.002     .0762443    .3400066
       ew_married |   .3599256   .0682711     5.27   0.000     .2261167    .4937344
        wb_anteil |  -.2435306    .019106   -12.75   0.000    -.2809776   -.2060835
          wb_ausl |   .0319864   .0151846     2.11   0.035     .0022251    .0617478
         wb_18t24 |  -.0473269   .0319382    -1.48   0.138    -.1099247    .0152708
         wb_25t34 |  -.0095468   .0178648    -0.53   0.593    -.0445611    .0254675
         wb_35t44 |  -.0196249   .0252076    -0.78   0.436     -.069031    .0297811
         wb_45t59 |   .0375531   .0245689     1.53   0.126    -.0106012    .0857073
          avg_dur |  -.0460054   .0231701    -1.99   0.047    -.0914179   -.0005928
          hh_kids |    .038006   .0449465     0.85   0.398    -.0500876    .1260996
mpreis_flats_rent |   .0827203   .0242626     3.41   0.001     .0351665     .130274
   ln_street_dist |  -3.262368   .5102349    -6.39   0.000     -4.26241   -2.262325
-----------------------------------------------------------------------------------

 ( 1)  - tau0 + tau1 = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0343687   .1884613    -0.18   0.855     -.403746    .3350086
------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.8112423   .2277747  -1.257681  -.3648039       2399        270 
    Effect_1 | -.9157674   .2470641  -1.400013  -.4315217       1871        167 
    Effect_2 | -.8110633   .2692329   -1.33876  -.2833669       1265        124 
     Average | -.8423181   .2039388  -1.242038  -.4425979       5535        561 
   Placebo_1 | -.1164187   .1208205  -.3532268   .1203894       2399        270 
   Placebo_2 |  .3791137   .1467927      .0914   .6668274       1791        260 
   Placebo_3 | -.0505834   .1253389  -.2962477   .1950809       1791        260 
   Placebo_4 | -.0519982   .1942392   -.432707   .3287107       1193         87 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      23.49
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9670
                                                  Adj R-squared   =     0.9616
                                                  Within R-sq.    =     0.2338
Number of clusters (sb_new)  =        618         Root MSE        =     1.7983

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.2382187    .356667    -0.67   0.504    -.9386472    .4622098
          F6event |  -.2137879   .3127797    -0.68   0.495    -.8280298    .4004539
          F5event |   .0249503   .2559896     0.10   0.922    -.4777662    .5276669
          F4event |  -.1492519   .1892787    -0.79   0.431    -.5209605    .2224566
          F3event |  -.0960296   .2044625    -0.47   0.639    -.4975563    .3054972
          F2event |    .162986    .139737     1.17   0.244    -.1114319    .4374038
          L0event |  -.6642396   .2228509    -2.98   0.003    -1.101878   -.2266013
          L1event |  -.6365066   .2254085    -2.82   0.005    -1.079168   -.1938457
          L2event |  -.4368401   .2393423    -1.83   0.068    -.9068645    .0331842
          L3event |  -.1444955   .2467509    -0.59   0.558    -.6290689    .3400779
          L4event |  -.1542154   .4606626    -0.33   0.738    -1.058872    .7504413
          L5event |   .4794252   .6752909     0.71   0.478    -.8467221    1.805572
          L6event |   .5574349   .8470786     0.66   0.511    -1.106072    2.220942
          L7event |   .9113192   1.202976     0.76   0.449    -1.451105    3.273744
          F1event |          0  (omitted)
        ln_ew_ges |  -1.302402   .9770042    -1.33   0.183    -3.221059    .6162548
         ew_biodt |   .3517144     .02714    12.96   0.000     .2984164    .4050125
        ew_dtmihi |   .0222568   .0501546     0.44   0.657    -.0762376    .1207513
         ew_ledig |   .2465072   .0534549     4.61   0.000     .1415316    .3514828
       ew_married |   .4134812   .0561785     7.36   0.000     .3031569    .5238054
        wb_anteil |  -.2453238   .0186414   -13.16   0.000     -.281932   -.2087155
          wb_ausl |   .0228956   .0143597     1.59   0.111    -.0053042    .0510954
         wb_18t24 |    -.03774   .0279976    -1.35   0.178    -.0927221    .0172421
         wb_25t34 |  -.0305538   .0153768    -1.99   0.047     -.060751   -.0003567
         wb_35t44 |  -.0038983   .0205017    -0.19   0.849    -.0441599    .0363632
         wb_45t59 |   .0148287   .0204633     0.72   0.469    -.0253575     .055015
          avg_dur |  -.0159716   .0214671    -0.74   0.457    -.0581291    .0261859
          hh_kids |  -.0168247   .0396639    -0.42   0.672    -.0947173    .0610679
mpreis_flats_rent |    .058033   .0195054     2.98   0.003      .019728    .0963379
   ln_street_dist |   -3.42898    .255426   -13.42   0.000    -3.930589    -2.92737
            _cons |   9.839251   8.918452     1.10   0.270    -7.674951    27.35345
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .027733   .1718881     0.16   0.872    -.3098236    .3652895
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 6,754,095.5338926)
(obs=4,346)

IW estimates for dynamic effects                        Number of obs =  4,666
Absorbing 2 HDFE groups                                 F(50, 617)    =  21.00
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.9677
                                                        Adj R-squared = 0.9623
                                                        Root MSE      = 1.7826
                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |   -.484584   .8378299    -0.58   0.563    -2.129928     1.16076
     F5event |   .6046688   .4211008     1.44   0.152    -.2222958    1.431633
     F4event |  -.3908522    .211289    -1.85   0.065    -.8057851    .0240806
     F3event |  -.2213153   .2256381    -0.98   0.327    -.6644272    .2217965
     F2event |   .1850142   .1489209     1.24   0.215    -.1074391    .4774675
     L0event |  -1.117986   .2603637    -4.29   0.000    -1.629292   -.6066793
     L1event |  -.9243837   .2430322    -3.80   0.000    -1.401654   -.4471131
     L2event |  -.6811004   .2500751    -2.72   0.007    -1.172202   -.1899989
     L3event |   -.480394   .2494657    -1.93   0.055    -.9702988    .0095109
     L4event |  -.3484389    .735245    -0.47   0.636    -1.792325    1.095447
     L5event |     .34177   .8784619     0.39   0.697    -1.383368    2.066908
     L6event |  -.4526732    1.59262    -0.28   0.776    -3.580285    2.674939
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .193602     .19381     1.00   0.318    -.1862586    .5734625
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 4,609
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0491293   .0480542     1.02   0.307    -.0450552    .1433138
    Post_avg |          0  (omitted)
         Tm6 |   .0392033   .1953364     0.20   0.841    -.3436491    .4220557
         Tm5 |   .3773763   .2600983     1.45   0.147    -.1324069    .8871596
         Tm4 |  -.1459676   .1823467    -0.80   0.423    -.5033605    .2114253
         Tm3 |  -.0328239   .1127158    -0.29   0.771    -.2537427    .1880949
         Tm2 |   .1116258    .141321     0.79   0.430    -.1653583    .3886098
         Tm1 |  -.0546383    .116856    -0.47   0.640    -.2836719    .1743954
         Tp0 |  -.9525476   .2351068    -4.05   0.000    -1.413348   -.4917468
         Tp1 |  -.9003934   .2840559    -3.17   0.002    -1.457133   -.3436541
         Tp2 |   -.765683   .3276163    -2.34   0.019    -1.407799   -.1235669
         Tp3 |  -.6681807   .3098436    -2.16   0.031    -1.275463   -.0608985
         Tp4 |  -.6864221   .7319759    -0.94   0.348    -2.121068    .7482243
         Tp5 |          0  (omitted)
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0491293          .        .       .            .           .
    Post_avg |  -.6622045          .        .       .            .           .
         Tm6 |   .0392033   .1953364     0.20   0.841    -.3436491    .4220557
         Tm5 |   .3773763   .2600983     1.45   0.147    -.1324069    .8871596
         Tm4 |  -.1459676   .1823467    -0.80   0.423    -.5033605    .2114253
         Tm3 |  -.0328239   .1127158    -0.29   0.771    -.2537427    .1880949
         Tm2 |   .1116258    .141321     0.79   0.430    -.1653583    .3886098
         Tm1 |  -.0546383    .116856    -0.47   0.640    -.2836719    .1743954
         Tp0 |  -.9525476   .2351068    -4.05   0.000    -1.413348   -.4917468
         Tp1 |  -.9003934   .2840559    -3.17   0.002    -1.457133   -.3436541
         Tp2 |   -.765683   .3276163    -2.34   0.019    -1.407799   -.1235669
         Tp3 |  -.6681807   .3098436    -2.16   0.031    -1.275463   -.0608985
         Tp4 |  -.6864221   .7319759    -0.94   0.348    -2.121068    .7482243
         Tp5 |          0  (omitted)
------------------------------------------------------------------------------
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 4,504
-----------------------------------------------------------------------------------
  turnout_pos_req | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |   .4610126   .2074137     2.22   0.026     .0544892     .867536
             tau1 |   .8743888    .215212     4.06   0.000      .452581    1.296197
             tau2 |   1.081884   .2730684     3.96   0.000     .5466793    1.617088
             pre1 |   .2978169   .2395951     1.24   0.214    -.1717809    .7674147
             pre2 |   .0942845   .2448204     0.39   0.700    -.3855547    .5741236
             pre3 |   .2433713   .2541488     0.96   0.338    -.2547511    .7414937
             pre4 |   .1745427    .171642     1.02   0.309    -.1618694    .5109547
        ln_ew_ges |   2.262465   1.552406     1.46   0.145    -.7801948    5.305125
         ew_biodt |   .4406864   .0339382    12.98   0.000     .3741687    .5072042
        ew_dtmihi |  -.1699157   .0720792    -2.36   0.018    -.3111884    -.028643
         ew_ledig |   .1912212   .0852516     2.24   0.025     .0241312    .3583112
       ew_married |   .2285468    .087626     2.61   0.009      .056803    .4002906
        wb_anteil |   -.308452   .0278775   -11.06   0.000    -.3630908   -.2538132
          wb_ausl |  -.0469892    .015292    -3.07   0.002    -.0769611   -.0170174
         wb_18t24 |  -.0203097   .0321567    -0.63   0.528    -.0833356    .0427162
         wb_25t34 |   .0234086   .0182059     1.29   0.199    -.0122744    .0590916
         wb_35t44 |   .0108486   .0266419     0.41   0.684    -.0413685    .0630657
         wb_45t59 |  -.0253823   .0239392    -1.06   0.289    -.0723023    .0215376
          avg_dur |   .0766439    .028461     2.69   0.007     .0208614    .1324264
          hh_kids |  -.1003809   .0534611    -1.88   0.060    -.2051627    .0044009
mpreis_flats_rent |   .0201564    .027209     0.74   0.459    -.0331722     .073485
   ln_street_dist |   2.370328   .6038821     3.93   0.000     1.186741    3.553916
-----------------------------------------------------------------------------------

 ( 1)  - tau0 + tau1 = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4133762   .1745505     2.37   0.018     .0712634    .7554889
------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |   .506388   .2417164   .0326239   .9801521       2399        270 
    Effect_1 |  1.001801   .2767442   .4593822    1.54422       1871        167 
    Effect_2 |  1.192968   .3018545   .6013336   1.784603       1265        124 
     Average |  .8056214   .2307781   .3532963   1.257946       5535        561 
   Placebo_1 |  .1490992   .1351383  -.1157719   .4139703       2399        270 
   Placebo_2 | -.1778533   .1978969  -.5657312   .2100245       1791        260 
   Placebo_3 |  .1503674   .1610784  -.1653463   .4660811       1791        260 
   Placebo_4 |  .3678249   .2081739   -.040196   .7758457       1193         87 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      21.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9487
                                                  Adj R-squared   =     0.9404
                                                  Within R-sq.    =     0.2447
Number of clusters (sb_new)  =        618         Root MSE        =     1.8999

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .2511641   .3178198     0.79   0.430    -.3729756    .8753038
          F6event |   .3559349   .2714134     1.31   0.190    -.1770712    .8889409
          F5event |  -.2734707   .2757317    -0.99   0.322    -.8149571    .2680158
          F4event |  -.0828914   .1675184    -0.49   0.621    -.4118667     .246084
          F3event |  -.0696513   .2004904    -0.35   0.728    -.4633775    .3240749
          F2event |   -.172367   .1419061    -1.21   0.225    -.4510446    .1063106
          L0event |     .22924   .2261913     1.01   0.311    -.2149582    .6734381
          L1event |   .6901577   .2188145     3.15   0.002     .2604462    1.119869
          L2event |   .7655458   .2562998     2.99   0.003       .26222    1.268872
          L3event |   .1201766   .2477701     0.49   0.628    -.3663983    .6067516
          L4event |   1.594515   .5964776     2.67   0.008     .4231428    2.765888
          L5event |   .9962226    .525721     1.89   0.059    -.0361968    2.028642
          L6event |  -.0936155   .5486369    -0.17   0.865    -1.171038    .9838067
          L7event |  -.4020801   .9380435    -0.43   0.668    -2.244225    1.440065
          F1event |          0  (omitted)
        ln_ew_ges |   2.821751   1.343357     2.10   0.036     .1836448    5.459858
         ew_biodt |   .4384215    .029803    14.71   0.000     .3798938    .4969492
        ew_dtmihi |  -.1979899   .0592522    -3.34   0.001    -.3143504   -.0816295
         ew_ledig |   .1420534   .0757689     1.87   0.061    -.0067427    .2908495
       ew_married |   .1995061    .077237     2.58   0.010     .0478268    .3511854
        wb_anteil |  -.2964858   .0255655   -11.60   0.000    -.3466918   -.2462799
          wb_ausl |   -.057306   .0136281    -4.20   0.000     -.084069   -.0305429
         wb_18t24 |   -.016797   .0278605    -0.60   0.547      -.07151    .0379159
         wb_25t34 |   .0218096    .015635     1.39   0.164    -.0088946    .0525138
         wb_35t44 |   -.004104   .0213259    -0.19   0.847    -.0459841    .0377761
         wb_45t59 |  -.0270054   .0195061    -1.38   0.167    -.0653117    .0113009
          avg_dur |   .0584075   .0232187     2.52   0.012     .0128102    .1040047
          hh_kids |  -.0656262   .0430168    -1.53   0.128    -.1501033     .018851
mpreis_flats_rent |    .047243    .020952     2.25   0.024     .0060971    .0883888
   ln_street_dist |   2.622052   .2566932    10.21   0.000     2.117954     3.12615
            _cons |  -10.72063   11.56172    -0.93   0.354    -33.42572    11.98447
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4609177   .1569218     2.94   0.003     .1527521    .7690834
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 6,754,095.5338926)
(obs=4,346)

IW estimates for dynamic effects                        Number of obs =  4,666
Absorbing 2 HDFE groups                                 F(50, 617)    =  16.24
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.9500
                                                        Adj R-squared = 0.9415
                                                        Root MSE      = 1.8812
                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |   .4415568    .514248     0.86   0.391    -.5683318    1.451445
     F5event |  -.2679406   .3681444    -0.73   0.467    -.9909086    .4550274
     F4event |   .1361188   .1636534     0.83   0.406    -.1852664     .457504
     F3event |   .0463341   .2191083     0.21   0.833    -.3839543    .4766226
     F2event |  -.2296864   .1569184    -1.46   0.144    -.5378452    .0784725
     L0event |   .7378263   .2487232     2.97   0.003     .2493796    1.226273
     L1event |   .8632622   .2302278     3.75   0.000     .4111372    1.315387
     L2event |   .9193118   .2574499     3.57   0.000     .4137275    1.424896
     L3event |    .290257   .2376534     1.22   0.222    -.1764507    .7569646
     L4event |   1.239429   .6611215     1.87   0.061    -.0588917    2.537751
     L5event |   .5078364   .8500909     0.60   0.550    -1.161586    2.177259
     L6event |   .4083019    .916984     0.45   0.656    -1.392486     2.20909
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1254359   .1892342     0.66   0.507    -.2454563    .4963281
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 4,609
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |  -.0419432   .0459563    -0.91   0.361    -.1320159    .0481296
    Post_avg |          0  (omitted)
         Tm6 |   .1455139    .154105     0.94   0.345    -.1565263     .447554
         Tm5 |  -.4710409   .2561193    -1.84   0.066    -.9730254    .0309437
         Tm4 |   .0298613   .1507162     0.20   0.843    -.2655369    .3252596
         Tm3 |   .1038104   .1021632     1.02   0.310    -.0964257    .3040465
         Tm2 |   .0921775   .1299759     0.71   0.478    -.1625706    .3469255
         Tm1 |  -.1519811   .1091876    -1.39   0.164    -.3659849    .0620227
         Tp0 |   .4380316    .206637     2.12   0.034     .0330305    .8430327
         Tp1 |   .7541571    .248602     3.03   0.002     .2669062    1.241408
         Tp2 |   .8091661   .3097533     2.61   0.009     .2020607    1.416271
         Tp3 |   .3643947    .311655     1.17   0.242    -.2464379    .9752273
         Tp4 |    .386175   1.220008     0.32   0.752    -2.004997    2.777347
         Tp5 |          0  (omitted)
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |  -.0419432          .        .       .            .           .
    Post_avg |   .4586541          .        .       .            .           .
         Tm6 |   .1455139    .154105     0.94   0.345    -.1565263     .447554
         Tm5 |  -.4710409   .2561193    -1.84   0.066    -.9730254    .0309437
         Tm4 |   .0298613   .1507162     0.20   0.843    -.2655369    .3252596
         Tm3 |   .1038104   .1021632     1.02   0.310    -.0964257    .3040465
         Tm2 |   .0921775   .1299759     0.71   0.478    -.1625706    .3469255
         Tm1 |  -.1519811   .1091876    -1.39   0.164    -.3659849    .0620227
         Tp0 |   .4380316    .206637     2.12   0.034     .0330305    .8430327
         Tp1 |   .7541571    .248602     3.03   0.002     .2669062    1.241408
         Tp2 |   .8091661   .3097533     2.61   0.009     .2020607    1.416271
         Tp3 |   .3643947    .311655     1.17   0.242    -.2464379    .9752273
         Tp4 |    .386175   1.220008     0.32   0.752    -2.004997    2.777347
         Tp5 |          0  (omitted)
------------------------------------------------------------------------------
Warning: part of the sample was dropped for the following coefficients because FE could not be imp
> uted: tau0 tau1 tau2.

                                                         Number of obs = 4,504
-----------------------------------------------------------------------------------
  turnout_tot_req | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
             tau0 |  -.2928523   .1548981    -1.89   0.059     -.596447    .0107424
             tau1 |   .0861563   .2087466     0.41   0.680    -.3229796    .4952921
             tau2 |   .4477314   .2658344     1.68   0.092    -.0732944    .9687573
             pre1 |   .2682139   .2517649     1.07   0.287    -.2252363     .761664
             pre2 |   .1980142   .2200387     0.90   0.368    -.2332537    .6292822
             pre3 |   .0850744   .2048231     0.42   0.678    -.3163715    .4865203
             pre4 |  -.0063386   .1899592    -0.03   0.973    -.3786518    .3659746
        ln_ew_ges |   .8942467   1.166323     0.77   0.443    -1.391704    3.180198
         ew_biodt |   .8181775   .0349607    23.40   0.000     .7496558    .8866992
        ew_dtmihi |  -.1123604   .0599767    -1.87   0.061    -.2299127    .0051918
         ew_ledig |   .3993469   .0759369     5.26   0.000     .2505133    .5481804
       ew_married |   .5884726   .0767224     7.67   0.000     .4380994    .7388457
        wb_anteil |  -.5519826   .0294719   -18.73   0.000    -.6097464   -.4942187
          wb_ausl |  -.0150028   .0141657    -1.06   0.290     -.042767    .0127614
         wb_18t24 |  -.0676366   .0307535    -2.20   0.028    -.1279124   -.0073607
         wb_25t34 |   .0138618   .0173451     0.80   0.424    -.0201339    .0478576
         wb_35t44 |  -.0087763   .0235447    -0.37   0.709    -.0549232    .0373705
         wb_45t59 |   .0121707   .0239768     0.51   0.612    -.0348229    .0591643
          avg_dur |   .0306386   .0267331     1.15   0.252    -.0217573    .0830344
          hh_kids |   -.062375    .040748    -1.53   0.126    -.1422397    .0174896
mpreis_flats_rent |   .1028767   .0260689     3.95   0.000     .0517826    .1539708
   ln_street_dist |  -.8920398   .4642401    -1.92   0.055    -1.801934     .017854
-----------------------------------------------------------------------------------

 ( 1)  - tau0 + tau1 = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3790085   .1781345     2.13   0.033     .0298714    .7281457
------------------------------------------------------------------------------
 
To estimate event-study/dynamic effects, 
we recommend using the much faster did_multiplegt_dyn command, available from the ssc repository.

DID estimators of the instantaneous treatment effect, of dynamic treatment effects if the
dynamic option is used, and of placebo tests of the parallel trends assumption if the placebo
option is used. The estimators are robust to heterogeneous effects, and to dynamic effects if
the robust_dynamic option is used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.3048536   .1710549  -.6401212   .0304141       2399        270 
    Effect_1 |  .0860344   .2235977  -.3522172   .5242859       1871        167 
    Effect_2 |  .3819059   .2755412  -.1581548   .9219665       1265        124 
     Average | -.0366959   .1742822  -.3782891   .3048973       5535        561 
   Placebo_1 |    .03268   .1344156  -.2307747   .2961346       2399        270 
   Placebo_2 |   .201261   .1535326  -.0996629    .502185       1791        260 
   Placebo_3 |  .0997836   .1329543  -.1608068   .3603739       1791        260 
   Placebo_4 |   .315827   .2689891  -.2113916   .8430456       1193         87 

When dynamic effects and first-difference placebos are requested, the command does
not produce a graph, because placebos estimators are DIDs across consecutive time periods,
while dynamic effects estimators are long-difference DIDs, so they are not really comparable.
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  29,    617) =      43.19
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9876
                                                  Adj R-squared   =     0.9856
                                                  Within R-sq.    =     0.4491
Number of clusters (sb_new)  =        618         Root MSE        =     1.7944

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0129449   .3205699     0.04   0.968    -.6165954    .6424852
          F6event |   .1421461   .2711827     0.52   0.600     -.390407    .6746992
          F5event |    -.24852   .2729302    -0.91   0.363    -.7845047    .2874647
          F4event |   -.232143   .1702714    -1.36   0.173    -.5665247    .1022388
          F3event |   -.165681   .1673897    -0.99   0.323    -.4944036    .1630415
          F2event |  -.0093805   .1530822    -0.06   0.951    -.3100058    .2912448
          L0event |  -.4349993   .1670525    -2.60   0.009    -.7630597   -.1069389
          L1event |   .0536516   .2040015     0.26   0.793    -.3469698    .4542731
          L2event |    .328706   .2437291     1.35   0.178    -.1499332    .8073452
          L3event |  -.0243185   .2523148    -0.10   0.923    -.5198185    .4711814
          L4event |   1.440299   .7560865     1.90   0.057    -.0445156    2.925114
          L5event |   1.475648    .678837     2.17   0.030     .1425368    2.808759
          L6event |   .4638191   .9364979     0.50   0.621    -1.375291    2.302929
          L7event |   .5092405   .8391238     0.61   0.544    -1.138644    2.157125
          F1event |          0  (omitted)
        ln_ew_ges |   1.519349   1.067014     1.42   0.155    -.5760697    3.614768
         ew_biodt |   .7901359   .0329695    23.97   0.000     .7253898     .854882
        ew_dtmihi |   -.175733   .0539184    -3.26   0.001    -.2816187   -.0698472
         ew_ledig |   .3885608   .0767774     5.06   0.000      .237784    .5393375
       ew_married |   .6129874   .0748371     8.19   0.000     .4660211    .7599537
        wb_anteil |  -.5418096    .027918   -19.41   0.000    -.5966355   -.4869838
          wb_ausl |  -.0344104   .0124898    -2.76   0.006     -.058938   -.0098827
         wb_18t24 |  -.0545371   .0266569    -2.05   0.041    -.1068863   -.0021879
         wb_25t34 |  -.0087442   .0145132    -0.60   0.547    -.0372454     .019757
         wb_35t44 |  -.0080023   .0193951    -0.41   0.680    -.0460908    .0300862
         wb_45t59 |  -.0121767   .0192422    -0.63   0.527    -.0499648    .0256115
          avg_dur |   .0424358    .022645     1.87   0.061    -.0020348    .0869065
          hh_kids |   -.082451   .0359297    -2.29   0.022    -.1530103   -.0118917
mpreis_flats_rent |    .105276   .0209694     5.02   0.000     .0640959     .146456
   ln_street_dist |  -.8069275   .2390101    -3.38   0.001    -1.276299   -.3375556
            _cons |  -.8813874   10.31926    -0.09   0.932    -21.14651    19.38374
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     wahl_id |         8           1           7     |
      sb_new |       618         618           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4886509   .1635443     2.99   0.003       .16748    .8098218
------------------------------------------------------------------------------

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
          Ei |      2,240       5.425    1.454905          1          8
(sum of wgt is 6,754,095.5338926)
(obs=4,346)

IW estimates for dynamic effects                        Number of obs =  4,666
Absorbing 2 HDFE groups                                 F(50, 617)    =  31.19
                                                        Prob > F      = 0.0000
                                                        R-squared     = 0.9877
                                                        Adj R-squared = 0.9857
                                                        Root MSE      = 1.7894
                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |          0  (omitted)
     F6event |  -.0430265   .7251446    -0.06   0.953    -1.467077    1.381024
     F5event |   .3367298   .3700787     0.91   0.363    -.3900368    1.063496
     F4event |  -.2547331    .184029    -1.38   0.167    -.6161321     .106666
     F3event |  -.1749813   .1768653    -0.99   0.323    -.5223122    .1723495
     F2event |  -.0446717   .1839264    -0.24   0.808    -.4058693     .316526
     L0event |  -.3801591   .1878068    -2.02   0.043    -.7489772    -.011341
     L1event |  -.0611211   .2142542    -0.29   0.776    -.4818769    .3596348
     L2event |   .2382116   .2442465     0.98   0.330    -.2414436    .7178668
     L3event |   -.190137   .2687219    -0.71   0.479    -.7178575    .3375834
     L4event |   .8909897   .6367754     1.40   0.162    -.3595202      2.1415
     L5event |   .8496061   .6629807     1.28   0.201    -.4523663    2.151578
     L6event |  -.0443729   1.345053    -0.03   0.974    -2.685811    2.597065
     L7event |          0  (omitted)
     F1event |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  - L0event + L1event = 0

------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .319038   .1677467     1.90   0.057    -.0097395    .6478156
------------------------------------------------------------------------------
(importance weights assumed)
(importance weights assumed)
Units always treated found. These will be ignored
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
...................................
Difference-in-difference with Multiple Time Periods

                                                         Number of obs = 4,609
Outcome model  : least squares
Treatment model: inverse probability
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0071862   .0514561     0.14   0.889    -.0936659    .1080384
    Post_avg |          0  (omitted)
         Tm6 |   .1847169   .1585834     1.16   0.244    -.1261009    .4955346
         Tm5 |  -.0936635   .3413128    -0.27   0.784    -.7626244    .5752973
         Tm4 |   -.116106   .1925757    -0.60   0.547    -.4935473    .2613354
         Tm3 |    .070986   .1198176     0.59   0.554    -.1638522    .3058242
         Tm2 |   .2038038   .1362185     1.50   0.135    -.0631795    .4707872
         Tm1 |  -.2066198   .1197353    -1.73   0.084    -.4412967     .028057
         Tp0 |  -.5145156   .1781334    -2.89   0.004    -.8636506   -.1653807
         Tp1 |  -.1462357   .2426136    -0.60   0.547    -.6217496    .3292782
         Tp2 |   .0434836   .2910647     0.15   0.881    -.5269927    .6139599
         Tp3 |  -.3037859   .3626127    -0.84   0.402    -1.014494     .406922
         Tp4 |  -.3002461    .971755    -0.31   0.757    -2.204851    1.604359
         Tp5 |          0  (omitted)
------------------------------------------------------------------------------
Control: Not yet Treated

See Callaway and Sant'Anna (2021) for details
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |   .0071862          .        .       .            .           .
    Post_avg |    -.20355          .        .       .            .           .
         Tm6 |   .1847169   .1585834     1.16   0.244    -.1261009    .4955346
         Tm5 |  -.0936635   .3413128    -0.27   0.784    -.7626244    .5752973
         Tm4 |   -.116106   .1925757    -0.60   0.547    -.4935473    .2613354
         Tm3 |    .070986   .1198176     0.59   0.554    -.1638522    .3058242
         Tm2 |   .2038038   .1362185     1.50   0.135    -.0631795    .4707872
         Tm1 |  -.2066198   .1197353    -1.73   0.084    -.4412967     .028057
         Tp0 |  -.5145156   .1781334    -2.89   0.004    -.8636506   -.1653807
         Tp1 |  -.1462357   .2426136    -0.60   0.547    -.6217496    .3292782
         Tp2 |   .0434836   .2910647     0.15   0.881    -.5269927    .6139599
         Tp3 |  -.3037859   .3626127    -0.84   0.402    -1.014494     .406922
         Tp4 |  -.3002461    .971755    -0.31   0.757    -2.204851    1.604359
         Tp5 |          0  (omitted)
------------------------------------------------------------------------------

. 
.         * PLOT: FIGURE D12. Event Study Results Absorbing the Distance Effect
.         grc1leg  turnout_urne turnout_pos_req turnout_tot_req , xcommon  col(2) iscale(.65) imar
> gins(small) pos(12)

.         gr_edit .style.editstyle declared_ysize(4) editcopy

.         gr_edit .legend.DragBy -60 40

.         gr_edit .legend.Edit , style(cols(1)) style(rows(0)) keepstyles

.         gr_edit .legend.title.text.Arrpush "Estimator:"

.         gr_edit .legend.title.style.editstyle size(small) editcopy

.         gr_edit .legend.title.DragBy 0 -15

.         graph export "$figures/Figure_D12_ES_novel_ctr_wgt_ln_dist.pdf", replace        
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_D12_ES
    > _novel_ctr_wgt_ln_dist.pdf saved as PDF format

. 
.                         
. * Table E5. Differences between t_1 and t_0, novel DiD estimators
.         /* append and export tex table */
.         frame testsAbs{
.                 drop p t 
.                 reshape wide delta, i(estimator) j(turnout) string
(j = turnout_pos_req turnout_tot_req turnout_urne)

Data                               Long   ->   Wide
-----------------------------------------------------------------------------
Number of observations               15   ->   5           
Number of variables                   3   ->   4           
j variable (3 values)           turnout   ->   (dropped)
xij variables:
                                  delta   ->   deltaturnout_pos_req deltaturnout_tot_req deltaturn
> out_urne
-----------------------------------------------------------------------------
.                 rename delta* *                                 
.                 list

     +-------------------------------------------------+
     |  estimator   turn~s_req   turn~t_req   turnou~e |
     |-------------------------------------------------|
  1. | BJS (2021)       0.413*       0.379*     -0.034 |
  2. |  CS (2021)        0.316        0.368      0.052 |
  3. |  SA (2020)        0.125        0.319      0.194 |
  4. |   TWFE-OLS      0.461**      0.489**      0.028 |
  5. | dCDH (2020       0.495*       0.391*     -0.105 |
     +-------------------------------------------------+
.                 set obs 6
Number of observations (_N) was 5, now 6.
.                 sort estimator
.                 replace estimator = "\multicolumn{4}{l}{\textit{Panel B: Differences based on ev
> ent study estimates conditional on log distance}}" if estimator==""
variable estimator was str10 now str108
(1 real change made)
.                 gen eq=_n*100
.                 set obs 7
Number of observations (_N) was 6, now 7.
.                 replace estimator = "\multicolumn{4}{l}{\textit{Panel A: Differences based on ev
> ent study estimates restricted to precincts with increased distance}} " if estimator==""
variable estimator was str108 now str129
(1 real change made)
.                 append using "$tmp/ES_test_tau1_tau2_only_dist_up"
.                 replace eq=_n if eq==.
(6 real changes made)
.                 sort eq 
.                 drop eq
.                 order estimator turnout_pos_req turnout_urne turnout_tot_req    
.                 texsave using "$tables/Table_E5_ES_tau1_tau0", ///
>                                                 align(lccc) hlines(1 -1) autonumber frag replace
>  noendash                                       
.         }                               

.                                         
. 
end of do-file
Running: 04f_rob_honestdid_figures_c6_c7.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output: Figure C.6, C.7
> 
> Task: RR (2023), honest DiD, robustness to violation of PT assumption
> 
> */      
.         
. * PULL: Precinct-level panel
.         use "$newdata/estimation_prep_ltw18.dta", clear

. 
. ********************************************************************************
.         //       Prep Estimation //
. ********************************************************************************
.         
.         // compute id for DISTANCE increase/decrease, 0 else
.         gen     tmp = (del_street_dist>0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase in event, 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease in event, 0 else"              

.                 
.         // set global for outcomes (saved in global b/c order must not change below)
.         global outcomes turnout_urne turnout_pos_req turnout_tot_req

.                 
.         
. ********************************************************************************
.                 // RR (2023), honest DID: Baseline Estimates //
. ********************************************************************************
.         
.  * 1) Estimate baseline Event Study 
.  
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         order F1event, last

.         
.         // Estimate baseline ES
.         estimates clear 

.  
.  foreach v of varlist $outcomes {
  2.         
.                 reghdfe `v' F7event-L7event F1event $ctr $wgt if smpl_trim==1, absorb(i.wahl_id#
> i.stadtbez i.sb_new) cluster(sb_new)
  3.                 
.         estimates store `v'
  4.  }
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      17.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9658
                                                  Within R-sq.    =     0.1691
Number of clusters (sb_new)  =        618         Root MSE        =     1.6979

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3609916    -0.46   0.646    -.8749677    .5428746
          F6event |   .0365542   .3229698     0.11   0.910    -.5976991    .6708075
          F5event |   .2427259   .2553086     0.95   0.342    -.2586533    .7441051
          F4event |   .0132213   .1747424     0.08   0.940    -.3299406    .3563833
          F3event |  -.0565081    .169943    -0.33   0.740    -.3902449    .2772287
          F2event |    .006289   .1214372     0.05   0.959    -.2321913    .2447694
          L0event |  -.9985596   .2347416    -4.25   0.000    -1.459549   -.5375702
          L1event |   -.892731   .2344548    -3.81   0.000    -1.353157   -.4323048
          L2event |  -.7515249   .2578216    -2.91   0.004    -1.257839   -.2452106
          L3event |  -.2931705   .2663112    -1.10   0.271    -.8161567    .2298157
          L4event |  -.8782313   .4731179    -1.86   0.064    -1.807348    .0508853
          L5event |  -.5487131   .6172865    -0.89   0.374     -1.76095    .6635241
          L6event |    1.07717   .7208125     1.49   0.136    -.3383731    2.492714
          L7event |   .9427971   1.187553     0.79   0.428    -1.389339    3.274933
          F1event |          0  (omitted)
        ln_ew_ges |  -.7878053   .9548651    -0.83   0.410    -2.662985    1.087374
         ew_biodt |    .373603   .0281093    13.29   0.000     .3184015    .4288045
        ew_dtmihi |   .0630129   .0513196     1.23   0.220    -.0377693    .1637951
         ew_ledig |   .2127399   .0574696     3.70   0.000     .0998801    .3255996
       ew_married |   .4312672   .0590852     7.30   0.000     .3152348    .5472997
        wb_anteil |  -.2895078   .0204524   -14.16   0.000    -.3296726   -.2493431
          wb_ausl |   .0153767   .0162706     0.95   0.345    -.0165758    .0473293
         wb_18t24 |  -.0164981   .0295896    -0.56   0.577    -.0746067    .0416105
         wb_25t34 |  -.0724461   .0193717    -3.74   0.000    -.1104886   -.0344037
         wb_35t44 |    .006101   .0230068     0.27   0.791    -.0390802    .0512822
         wb_45t59 |   .0140084   .0220328     0.64   0.525      -.02926    .0572768
          avg_dur |  -.0288813   .0210954    -1.37   0.171    -.0703088    .0125462
          hh_kids |  -.0506236   .0408723    -1.24   0.216    -.1308893    .0296421
mpreis_flats_rent |   .0303932   .0255105     1.19   0.234    -.0197047    .0804911
            _cons |   11.36038   9.043266     1.26   0.210    -6.398933    29.11969
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9616
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2040
Number of clusters (sb_new)  =        618         Root MSE        =     1.6847

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3314703     0.25   0.801    -.5673098    .7345839
          F6event |   .2184141   .2600152     0.84   0.401    -.2922081    .7290362
          F5event |  -.4796951   .2637694    -1.82   0.069    -.9976898    .0382995
          F4event |  -.2325114   .1614186    -1.44   0.150    -.5495078     .084485
          F3event |   .0074853   .1524588     0.05   0.961    -.2919157    .3068862
          F2event |   -.059996   .1248273    -0.48   0.631    -.3051339    .1851419
          L0event |   .6092887   .2189284     2.78   0.006     .1793535    1.039224
          L1event |   .9038027   .2273231     3.98   0.000      .457382    1.350223
          L2event |   1.047491   .2631015     3.98   0.000     .5308081    1.564174
          L3event |   .4175165   .2577372     1.62   0.106    -.0886321    .9236651
          L4event |   1.531036   .6502778     2.35   0.019     .2540104    2.808063
          L5event |   2.356691   .5459981     4.32   0.000     1.284451    3.428931
          L6event |  -.3848372    .877942    -0.44   0.661    -2.108954     1.33928
          L7event |   -.503072    .765562    -0.66   0.511    -2.006495    1.000351
          F1event |          0  (omitted)
        ln_ew_ges |   2.465914   1.349016     1.83   0.068    -.1833058    5.115133
         ew_biodt |    .388145   .0296323    13.10   0.000     .3299526    .4463375
        ew_dtmihi |  -.2303872   .0595829    -3.87   0.000     -.347397   -.1133774
         ew_ledig |   .2111155   .0802108     2.63   0.009     .0535962    .3686348
       ew_married |   .2074512   .0799261     2.60   0.010     .0504911    .3644114
        wb_anteil |  -.2425344   .0223631   -10.85   0.000    -.2864513   -.1986174
          wb_ausl |   -.068913   .0145836    -4.73   0.000    -.0975526   -.0402734
         wb_18t24 |  -.0273058   .0275422    -0.99   0.322    -.0813937    .0267821
         wb_25t34 |   .0526598   .0194716     2.70   0.007     .0144212    .0908985
         wb_35t44 |  -.0089296   .0249211    -0.36   0.720    -.0578702    .0400109
         wb_45t59 |   -.038132   .0205362    -1.86   0.064    -.0784614    .0021973
          avg_dur |   .0459808   .0233899     1.97   0.050     .0000472    .0919144
          hh_kids |  -.0642629   .0417916    -1.54   0.125    -.1463339    .0178081
mpreis_flats_rent |  -.0155286   .0234069    -0.66   0.507    -.0614954    .0304383
            _cons |  -11.72708   11.35784    -1.03   0.302     -34.0318    10.57764
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4395
Number of clusters (sb_new)  =        618         Root MSE        =     1.6245

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3259399    -0.25   0.800    -.7224958    .5576764
          F6event |   .2549675   .2761707     0.92   0.356    -.2873811    .7973161
          F5event |  -.2369689   .2619342    -0.90   0.366    -.7513594    .2774217
          F4event |  -.2192898   .1694872    -1.29   0.196    -.5521315     .113552
          F3event |  -.0490231   .1579758    -0.31   0.756    -.3592586    .2612124
          F2event |  -.0537065   .1322374    -0.41   0.685    -.3133964    .2059834
          L0event |  -.3892706   .1633916    -2.38   0.018    -.7101417   -.0683996
          L1event |    .011072    .200952     0.06   0.956    -.3835608    .4057048
          L2event |   .2959665    .224252     1.32   0.187    -.1444233    .7363563
          L3event |   .1243464   .2332215     0.53   0.594    -.3336578    .5823505
          L4event |   .6528057   .6207241     1.05   0.293    -.5661824    1.871794
          L5event |   1.807978   .7132727     2.53   0.011      .407241    3.208714
          L6event |   .6923315   .7952045     0.87   0.384     -.869304    2.253967
          L7event |   .4397263   1.099927     0.40   0.689    -1.720328    2.599781
          F1event |          0  (omitted)
        ln_ew_ges |   1.678109   1.068642     1.57   0.117    -.4205078    3.776725
         ew_biodt |    .761748   .0314252    24.24   0.000     .7000348    .8234612
        ew_dtmihi |  -.1673742   .0522282    -3.20   0.001    -.2699407   -.0648076
         ew_ledig |   .4238555   .0699086     6.06   0.000     .2865679    .5611432
       ew_married |   .6387186   .0688582     9.28   0.000     .5034938    .7739433
        wb_anteil |  -.5320422   .0239517   -22.21   0.000     -.579079   -.4850055
          wb_ausl |  -.0535363   .0175921    -3.04   0.002     -.088084   -.0189885
         wb_18t24 |  -.0438039    .025903    -1.69   0.091    -.0946727    .0070648
         wb_25t34 |  -.0197863   .0166674    -1.19   0.236     -.052518    .0129454
         wb_35t44 |  -.0028286   .0208991    -0.14   0.892    -.0438706    .0382134
         wb_45t59 |  -.0241236   .0196706    -1.23   0.221    -.0627531    .0145059
          avg_dur |   .0170995    .022054     0.78   0.438    -.0262106    .0604096
          hh_kids |  -.1148866    .035755    -3.21   0.001    -.1851028   -.0446704
mpreis_flats_rent |   .0148646   .0236111     0.63   0.529    -.0315033    .0612326
            _cons |   -.366715   10.03193    -0.04   0.971    -20.06759    19.33416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
.  * 2) Extract estimates: STORES coefs + 95% CI in new vars: __event_H, __event_coef, __event_hi,
>  __event_lo (with index= 1,2, and 3)
.         event_plot  $outcomes, ///
>         stub_lag(L#event) stub_lead(F#event) trimlead(4) trimlag(2)  savecoef noplot

.          
.          
.  * 3) Compute Roth & Ramabach (ReStud, 2023) CI for post-treatment coefs
.  cap drop __M*

.  local c=1                                                      // counter for outcome variables
> , 1=inperson, 2=mail-in, 3=total

.  foreach v of varlist $outcomes {
  2.          // LOAD estimates
.          qui estimates restore `v'
  3.          
.          // store "honest" CI here
.          gen __M_lb`c' =. 
  4.          gen __M_ub`c' =.
  5.          
.          // set M; M=0 with delta=sd means allowing for linear violations of PT 
.          local M=0
  6.          
.          // Loop over 3 post-treatment Coefs (in chronological order these are estimates 7,8, an
> d 9)
.          forvalues t=0/2 {
  7.                 
.                 * HonestEventStudy 
.                 
.                 // set l_vec matrix to obtain results for coef of interest (i.e. t=0, t=1, t=2),
>  e.g. (1 \ 0 \ 0) for t=0
.                 matrix l_vec = cond(`t'==0,1,0) \ cond(`t'==1,1,0) \ cond(`t'==2,1,0)
  8.                 honestdid, pre(4/6) post(7/9) mvec(`M') delta(sd) l_vec(l_vec)
  9.                 
.                 // extract "honest" CI and in variables
.                 mata: st_local("lb", strofreal(`s(HonestEventStudy)'.CI[2,2]))  // extract lower
>  honest CI bound
 10.                 mata: st_local("ub", strofreal(`s(HonestEventStudy)'.CI[2,3]))  // extract up
> per honest CI bound
 11.                 replace __M_lb`c'=  `lb' if __event_H1== `t'
 12.                 replace __M_ub`c'=  `ub' if __event_H1== `t'
 13.                 
.         }
 14.         local ++c
 15.  }      
(4,944 missing values generated)
(4,944 missing values generated)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -1.459 | -0.538 | (Original)
|  0.0000 | -1.480 | -0.456 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -1.352 | -0.433 | (Original)
|  0.0000 | -1.416 | -0.302 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -1.257 | -0.246 | (Original)
|  0.0000 | -1.368 | -0.059 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)
(4,944 missing values generated)
(4,944 missing values generated)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . |  0.180 |  1.038 | (Original)
|  0.0000 |  0.030 |  0.983 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . |  0.458 |  1.349 | (Original)
|  0.0000 |  0.190 |  1.268 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . |  0.532 |  1.563 | (Original)
|  0.0000 |  0.120 |  1.431 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)
(4,944 missing values generated)
(4,944 missing values generated)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.710 | -0.069 | (Original)
|  0.0000 | -0.844 | -0.125 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.383 |  0.405 | (Original)
|  0.0000 | -0.649 |  0.366 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.144 |  0.735 | (Original)
|  0.0000 | -0.543 |  0.660 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

.          
.  * 4) Plot results with bsl. CI and "honest" CI  
.         forvalues j = 1/3 {
  2.                 if `j'==1{
  3.                         local ttl "title({bf:Panel A.} Effect on Polling Place Turnout, just(
> left) span bexpand size(medium))"
  4.                 }
  5.                 if `j'==2{
  6.                         local ttl "title({bf:Panel B.} Effect on Mail-in Turnout, just(left) 
> span bexpand size(medium))"
  7.                 }
  8.                 if `j'==3 {
  9.                         local ttl "title({bf:Panel C.} Effect on Total Turnout, just(left) sp
> an bexpand size(medium))"
 10.                 }
 11.                 
.                 tw (connect __event_coef`j' __event_H`j' , sort lcol(black) ms(O) msize(2.5pt) m
> col(black)) ///
>                         (rcap __event_lo`j' __event_hi`j' __event_H`j', col(black)) ///
>                         (rcap __M_lb`j' __M_ub`j' __event_H`j', col(red) lpat(-)) ///
>                         , `ttl' ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel
> (-4(1)2) xtitle("Election since reassignment", size(medsmall)) ///
>                         xline(-0.5, lcol(black) lpat(solid)) yline(0, lcolor(gray) lpat(solid)) 
> name(gr`j', replace) ///
>                         legend(order(2 "95% CI" 3 "95% CI due to RR (2023) allowing for" "linear
>  violations of PT assumption") pos(12))
 12.         }                       

.                                 
.         * PLOT: FIGURE C6. Rob to linear violations of PT assumption–Pooled Reassignments       
>                       
.         grc1leg2  gr1 gr2 gr3 , xcommon  col(2) iscale(.8) name(bsl_honestdid, replace) ///
>                 pos(4) ring(0) lcol(1) imargins(small)  ///
>                                         lxoffset(0) lyoffset(19) legscale(*.9)
-grc1leg2- working...

Note: To preserve in a saved graphics file the legend's row and column rearrangements
made with the options -lrows()- and/or -lcols()-, specify the suboption -asis-
for the saving() option or for the graph save command.


.         graph export "$figures/Figure_C6_ES_RR_2023_bsl.pdf",replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C6_ES_
    > RR_2023_bsl.pdf saved as PDF format

.         
.         cap drop __*

.         
. * 5) breakdown value for manuscript 
.         estimates restore turnout_tot_req
(results turnout_tot_req are active now)

.         matrix l_vec = 1 \ 0 \ 0

.         honestdid, pre(4/6) post(7/9) mvec(0(.005).1) delta(sd) l_vec(l_vec) 

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.710 | -0.069 | (Original)
|  0.0000 | -0.844 | -0.125 | 
|  0.0050 | -0.845 | -0.124 | 
|  0.0100 | -0.846 | -0.120 | 
|  0.0150 | -0.848 | -0.114 | 
|  0.0200 | -0.851 | -0.107 | 
|  0.0250 | -0.854 | -0.098 | 
|  0.0300 | -0.857 | -0.088 | 
|  0.0350 | -0.861 | -0.076 | 
|  0.0400 | -0.866 | -0.065 | 
|  0.0450 | -0.870 | -0.053 | 
|  0.0500 | -0.874 | -0.041 | 
|  0.0550 | -0.878 | -0.028 | 
|  0.0600 | -0.882 | -0.016 | 
|  0.0650 | -0.886 | -0.004 | 
|  0.0700 | -0.890 |  0.007 | 
|  0.0750 | -0.893 |  0.019 | 
|  0.0800 | -0.896 |  0.030 | 
|  0.0850 | -0.899 |  0.041 | 
|  0.0900 | -0.901 |  0.051 | 
|  0.0950 | -0.904 |  0.061 | 
|  0.1000 | -0.911 |  0.066 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)

. 
.         
. 
. *********************************************************************************
.         // RR (2023) sensitvity for heterogeneity by distance increase/ decrease //
. *********************************************************************************       
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_dn                            // a 
> := decrease
  3.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b = F`l'event *ind_dist_up                         
>    // b:= increase
  5.                 lab var F`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6. 
.                 assert  F`l'event_b+F`l'event_a==F`l'event              
  7.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_dn    // a := decrease
  3.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b = L`l'event *ind_dist_up    // b:= increase
  5.                 lab var L`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 assert  L`l'event_b+L`l'event_a==L`l'event      
  7.         }

.         // ORDER dummies
.         order *event_b, last

.         order F1event*,last     

.         
.  * 1) Estimate event studies    
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         foreach v of varlist $outcomes {
  2.         
.                 qui reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b ///
>                                         $ctr $wgt if smpl_trim==1 , absorb(i.wahl_id#i.stadtbez 
> i.sb_new) cluster(sb_new)
  3. 
.                 estimates store `v'
  4.         }

.         
.         
.  * 2) Extract estimates: STORES coefs + 95% CI in new vars: __event_H, __event_coef, __event_hi,
>  __event_lo (with index=1_a, 1_b, 2_a, etc.)
.  * ADD suffix _a for DECREASE; _b for INCREASE
.  
.         // distance decrease
.         event_plot  $outcomes, ///
>         stub_lag(L#event_a) stub_lead(F#event_a) trimlead(4) trimlag(2)  savecoef noplot

.         ren __* _*

.         ren _* =_a

.         
.         // distance increase
.         event_plot  $outcomes, ///
>         stub_lag(L#event_b) stub_lead(F#event_b) trimlead(4) trimlag(2)  savecoef noplot

.         ren __* =_b     // (reverse order here so not to rename _a_b)

.         ren __* _*

.         
.         
.  * 3) Compute Roth & Ramabach (ReStud, 2023) CI for post-treatment coefs
.  cap drop _M*

.  local c=1                                                      // counter for outcome variables
> , 1=inperson, 2=mail-in, 3=total

.  foreach v of varlist $outcomes {
  2.          // LOAD estimates
.          qui estimates restore `v'
  3.          
.          // store "honest" CI here
.          gen _M_lb`c'_a =. 
  4.          gen _M_ub`c'_a =.
  5.          gen _M_lb`c'_b =. 
  6.          gen _M_ub`c'_b =. 
  7.          
.          // set M; M=0 with delta=sd means allowing for linear violations of PT 
.          local M=0
  8.          
.          // Loop over 3 post-treatment Coefs (in chronological order these are estimates 7,8, an
> d 9)
.          forvalues t=0/2 {
  9.                 
.                 * HonestEventStudy 
.                 
.                 // set l_vec matrix to obtain results for coef of interest (i.e. t=0, t=1, t=2),
>  e.g. (1 \ 0 \ 0) for t=0
.                 matrix l_vec = cond(`t'==0,1,0) \ cond(`t'==1,1,0) \ cond(`t'==2,1,0)
 10.                 
.                 * Decrease: coefs 4--6 (preperiods), coefs 7--9 (postperiods)
.                         honestdid, pre(4/6) post(7/9) mvec(`M') delta(sd) l_vec(l_vec)  
 11. 
.                         // extract results and store in variables
.                         mata: st_local("lb", strofreal(`s(HonestEventStudy)'.CI[2,2]))  // extra
> ct lower honest CI bound
 12.                         mata: st_local("ub", strofreal(`s(HonestEventStudy)'.CI[2,3]))  // ex
> tract upper honest CI bound
 13.                         replace _M_lb`c'_a=  `lb' if _event_H1_a== `t'
 14.                         replace _M_ub`c'_a=  `ub' if _event_H1_a== `t'
 15.                 
.                 * Increase: coefs 18--20 (preperiods), coefs 21--23 (postperiods)
.                         honestdid, pre(18/20) post(21/23) mvec(`M') delta(sd) l_vec(l_vec)      
 16.                         
.                         // extract results and store in variables
.                         mata: st_local("lb", strofreal(`s(HonestEventStudy)'.CI[2,2]))  // extra
> ct lower honest CI bound
 17.                         mata: st_local("ub", strofreal(`s(HonestEventStudy)'.CI[2,3]))  // ex
> tract upper honest CI bound                
 18.                         replace _M_lb`c'_b=  `lb' if _event_H1_a== `t'
 19.                         replace _M_ub`c'_b=  `ub' if _event_H1_a== `t'
 20.                 
.         }
 21.         
.         local ++c
 22.  }              
(4,944 missing values generated)
(4,944 missing values generated)
(4,944 missing values generated)
(4,944 missing values generated)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.200 |  1.152 | (Original)
|  0.0000 | -0.072 |  1.303 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -2.419 | -1.366 | (Original)
|  0.0000 | -2.430 | -1.235 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.013 |  1.219 | (Original)
|  0.0000 | -0.121 |  1.377 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -2.498 | -1.431 | (Original)
|  0.0000 | -2.557 | -1.241 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.192 |  1.170 | (Original)
|  0.0000 | -0.376 |  1.344 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -2.200 | -0.983 | (Original)
|  0.0000 | -2.301 | -0.705 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)
(4,944 missing values generated)
(4,944 missing values generated)
(4,944 missing values generated)
(4,944 missing values generated)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -1.064 |  0.144 | (Original)
|  0.0000 | -1.394 | -0.088 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . |  0.752 |  1.765 | (Original)
|  0.0000 |  0.615 |  1.736 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -1.004 |  0.215 | (Original)
|  0.0000 | -1.467 |  0.036 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . |  1.295 |  2.343 | (Original)
|  0.0000 |  1.040 |  2.306 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.648 |  0.746 | (Original)
|  0.0000 | -1.311 |  0.511 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . |  1.084 |  2.362 | (Original)
|  0.0000 |  0.702 |  2.296 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)
(4,944 missing values generated)
(4,944 missing values generated)
(4,944 missing values generated)
(4,944 missing values generated)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.454 |  0.486 | (Original)
|  0.0000 | -0.676 |  0.395 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -1.027 | -0.241 | (Original)
|  0.0000 | -1.135 | -0.267 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.343 |  0.759 | (Original)
|  0.0000 | -0.788 |  0.658 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.639 |  0.349 | (Original)
|  0.0000 | -0.866 |  0.392 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.063 |  1.140 | (Original)
|  0.0000 | -0.774 |  0.930 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

|    M    |   lb   |   ub   |
| ------- | ------ | ------ |
|       . | -0.414 |  0.677 | (Original)
|  0.0000 | -0.716 |  0.758 | 
(method = FLCI, Delta = DeltaSD, alpha = 0.050)
(1 real change made)
(1 real change made)

.         
. 
.  * 4) Plot results with bsl. CI and "honest" CI         
.         forvalues c = 1/3 {
  2.                 
.                 tw (connect _event_coef`c'_a _event_H`c'_a , sort lcol(black) ms(O) msize(2.5pt)
>  mcol(black)) ///
>                         (rcap _event_lo`c'_a _event_hi`c'_a _event_H`c'_a, col(black)) ///
>                         (rcap _M_lb`c'_a _M_ub`c'_a _event_H`c'_a, col(red) lpat(-)) ///
>                         , xtitle("") ytitle("Voter turnout in %""(estimates)", size(medsmall)) x
> label(-4(1)2) xtitle("Election since reassignment", size(medsmall)) ///
>                         xline(-0.5, lcol(black) lpat(solid)) yline(0, lcolor(gray) lpat(solid)) 
> name(gr`c'_a, replace) ///
>                         subtitle("{bf:b.} Distance decrease",nobox justification(left) size(meds
> mall)) ///
>                         legend(order(2 "95% CI" 3 "95% CI due to RR (2023) allowing for" "linear
>  violations of PT assumption") pos(12) row(1))
  3.                         
.                 
.                 tw (connect _event_coef`c'_b _event_H`c'_b , sort lcol(black) ms(O) msize(2.5pt)
>  mcol(black)) ///
>                         (rcap _event_lo`c'_b _event_hi`c'_b _event_H`c'_b, col(black)) ///
>                         (rcap _M_lb`c'_b _M_ub`c'_b _event_H`c'_b, col(red) lpat(-)) ///
>                         , xtitle("") ytitle("Voter turnout in %""(estimates)", size(medsmall)) x
> label(-4(1)2) xtitle("Election since reassignment", size(medsmall)) ///
>                         xline(-0.5, lcol(black) lpat(solid)) yline(0, lcolor(gray) lpat(solid)) 
> name(gr`c'_b, replace) ///
>                         subtitle("{bf:a.} Distance increase",nobox justification(left) size(meds
> mall)) ///
>                         legend(order(2 "95% CI" 3 "95% CI due to RR (2023) allowing for" "linear
>  violations of PT assumption") pos(12) row(1))
  4.         }       

.                         
.         
.         * PLOT: FIGURE C7. Robustness to linear violations of PT assumption– Effects by Distance
>  Change
.         grc1leg2 gr1_b gr1_a,            name(g1, replace) ///
>                         title("{bf:Panel A.} Effect on Polling Place Turnout", just(left) bexpan
> d size(small)) loff  iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 gr2_b gr2_a, name(g2, replace) ///
>                         title("{bf:Panel B.} Effect on Mail-in Turnout", just(left) bexpand size
> (small)) loff  iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 gr3_b gr3_a, name(g3, replace) ///
>                         title("{bf:Panel C.} Effect on Total Turnout", just(left) bexpand size(s
> mall))   iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 g1 g2 g3, col(1) imargins(zero) legscale(*.65) lrow(1)
-grc1leg2- working...

Note: To preserve in a saved graphics file the legend's row and column rearrangements
made with the options -lrows()- and/or -lcols()-, specify the suboption -asis-
for the saving() option or for the graph save command.


.         gr_edit .style.editstyle declared_ysize(6) editcopy                                     
>                                 

.         graph export "$figures/Figure_C7_ES_RR_2023_dist2.pdf",replace  
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C7_ES_
    > RR_2023_dist2.pdf saved as PDF format

.         
.                         
.                                 
. 
end of do-file
Running: 04g_rob_matching_figures_c3_c4_c5.do

. /*
> Inputs:  > prepared precinct panel [newdata/estimation_prep_ltw18]
>                  > arc_anly/arc_output/ltw18_sb_intersected 
>                         [Outcome of local matching (neighboring precincts), performed in ArcGIS]
>                         
> Output: Figure C.3, C.4, C.5
>                         
> Tasks:
>                 Event study analysis on matched samples using
>                 -       Local matching
>                 -       Mahalanobis distance matching
>                 -       Propensity score matching
>                 -       Entropy Balancing
>                 
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
. ********************************************************************************
.         //       Prep Estimation //
. ********************************************************************************
. 
. 
. *** Prep sample for local matching ---------------------------------------------                
. frame copy default tmp, replace 

. frame tmp{              
. * PULL: Precinct-level matched sample (with replacement) from ArcGIS
. *       k nearest neighbors where k=0 if treated unit is surrounded only by other treated units
. *               and k>=1 if treated unit shares precinct border with at least one never-treated 
> (control) unit
. *       sb_new := ID of treated precincts with matches (some treated units have no matched contr
> ols)
. *       sb_new_1 := IDs control precincts matched to treated precincts 
. 
.         import dbase "$arc_anly/arc_output/ltw18_sb_intersected", clear
(8 vars, 662 obs)
.         
.         // drop duplicates (some treated and control units share more than one edge)
.         duplicates drop

Duplicates in terms of all variables

(4 observations deleted)
.         
.         // gen lmatch_id := ID for cluster of treated unit and matched control units 
.         gen      lmatch_id = sb_new
.         lab var  lmatch_id "ID for 1:k matches of local matching"
.         
.         // save list of distinct treated units with matches (N=248)
.         tempvar tmp 
.         tempfile distinct_T
.         bys sb_new: gen `tmp'= (_n==1)
.         savesome sb_new lmatch_id T if `tmp'==1 using `distinct_T'
file C:\Users\Alipour\AppData\Local\Temp\9\ST_77c8_000001.tmp saved as .dta format
.                 
.         // List of matched control precincts
.         keep sb_new_1 lmatch_id T_1
.         ren  sb_new_1 sb_new 
.         ren      T_1 T   
.         
.         // append distinct treated units 
.         append using `distinct_T'
.         lab var T "indicator for treated units"
.         
.         // gen district id
.         gen stadtbez = floor(sb_new/100)
.         gen lm_stadtbez = floor(lmatch_id/100)
.         
.         // merge with data
.         expand  8 // expand to match 8 elections
(6,342 observations created)
.         bys sb_new lmatch_id: gen wahl_id=_n
. 
.         // gen sample ID of local matching 
.         gen smpl_lmatch = 1
.         lab var smpl_lmatch "ID treated/control units of local matching"
.         
.         // save 1:k (adjacent) neighbor matching
.         save "$tmp/local_matching_sample_k.dta", replace
(file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/tmp/local_matching_sample_k.dta
    not found)
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/tmp/local_matching_sample_k.dta
    saved
.         
.         // save 1:1 neighbor matching (simply ids treated and control units)
.         //              Note: may include control units matched to treated units in DIFFERENT di
> strict
.         //              Note: for untreated units matched to several treated, lmatch_id is rando
> mly assigned
.         tempvar tmp1 
.         bys sb_new wahl_id: gen `tmp1'= (_n==1) 
.         drop lmatch_id
.         savesome sb_new T smpl_lmatch wahl_id if `tmp1'==1 using "$tmp/local_matching_sample_1.d
> ta", replace
(file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/tmp/local_matching_sample_1.dta
    not found)
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/tmp/local_matching_sample_1.dta
    saved
.         cap drop `tmp1'
.         
.         // save 1:1 neighbor matching excluding cases where control unit is matched to treated i
> n DIFF district 
.         drop if lm_stadtbez !=stadtbez
(1,208 observations deleted)
.         
.         duplicates drop sb_new wahl_id, force 

Duplicates in terms of sb_new wahl_id

(2,032 observations deleted)
.         drop *stadtbez
.         
.         save "$tmp/local_matching_sample_1_within_district.dta", replace
(file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/tmp/local_matching_sample_1_wit
    > hin_district.dta not found)
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/tmp/local_matching_sample_1_wit
    > hin_district.dta saved
.         
. } 

.         
. *-------------------------------------------------------------------------------
. 
. *** Prep sample
.         // compute id for DISTANCE increase/decrease, 0 else
.         cap drop tmp*

.         gen     tmp = (del_street_dist>0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase in event, 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease in event, 0 else"              

.         
. 
. * TWFE OLS
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_dn                            // a 
> := decrease
  3.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b = F`l'event *ind_dist_up                         
>    // b:= increase
  5.                 lab var F`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6. 
.                 assert  F`l'event_b+F`l'event_a==F`l'event              
  7.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_dn    // a := decrease
  3.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b = L`l'event *ind_dist_up    // b:= increase
  5.                 lab var L`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 assert  L`l'event_b+L`l'event_a==L`l'event
  7.         }               

.         
.         // ORDER dummies
.         order *event_b, last

.         order F1event*,last             

.         
.         
.  *** Prepare weights and matched samples based on PSM, Mahanabolis, and Entropy bal
.  * Note: since we match on pre-treatment outcomes, we drop the 10 precincts that were
.  *                      treated in the first eleciton in our panel
.         
.         
. // Balancing vars       
. global ebal ew_ges ew_biodt ew_dtmihi ew_ledig ew_married wb_anteil ///
>                         wb_18t24 wb_25t34 wb_35t44 wb_45t59 avg_dur hh_kids mpreis_flats_rent //
> /
>                                 share_mail turnout_tot_req  shr_spd ///
>                                 street_dist     

.         
.  * 1) Entropy weights : fits weights s.t. 1st & 2nd moments of cov. distr of NT match T
.  *              > Exclude units treated in 2013 (no pre-treatment observations)
.  *              > include #eligible voters as 'base weights' for regressions
.         
.         local ncov :word count $ebal

.         local nt = "2 "*`ncov'

.         
.         ebalance T $ebal if wahl_id==1 &Ei!=1, wttreat basewt(wahlber_gesamt) target(`nt')


Data Setup
Treatment variable:   T
Covariate adjustment: ew_ges ew_biodt ew_dtmihi ew_ledig ew_married wb_anteil wb_18t24 wb_25t34 wb
> _35t44 wb_45t59 avg_dur hh_kids mpreis_flats_rent share_mail turnout_tot_req shr_spd street_dist
>  (1st order). ew_ges ew_biodt ew_dtmihi ew_ledig ew_married wb_anteil wb_18t24 wb_25t34 wb_35t44
>  wb_45t59 avg_dur hh_kids mpreis_flats_rent share_mail turnout_tot_req shr_spd street_dist (2nd 
> order).

Optimizing...
Iteration 1: Max Difference = 4338304.54
Iteration 2: Max Difference = 1595970.69
Iteration 3: Max Difference = 587122.44
Iteration 4: Max Difference = 215987.911
Iteration 5: Max Difference = 79455.1476
Iteration 6: Max Difference = 29227.5512
Iteration 7: Max Difference = 10749.8516
Iteration 8: Max Difference = 3952.28715
Iteration 9: Max Difference = 1451.60664
Iteration 10: Max Difference = 531.667723
Iteration 11: Max Difference = 193.268193
Iteration 12: Max Difference = 68.8502583
Iteration 13: Max Difference = 23.2658736
Iteration 14: Max Difference = 6.92699179
Iteration 15: Max Difference = 1.62494403
Iteration 16: Max Difference = .253632541
Iteration 17: Max Difference = .017317126
Iteration 18: Max Difference = .000169949
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 270     total of weights: 398561
Control units: 338     total of weights: 398561


Before: wahlber_gesamt as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
      ew_ges |      2362     133875      1.572 |      2350     120132      1.514 
    ew_biodt |     62.28      117.4     -.7574 |     62.42      113.9     -.5901 
   ew_dtmihi |     13.55      16.11      2.063 |     14.01      16.82      1.476 
    ew_ledig |     50.53      55.62      .3244 |     48.02      53.74      .7644 
  ew_married |     36.11      42.12   -.009557 |     38.43      44.89     -.3904 
   wb_anteil |     63.92      63.42     -1.077 |     63.82      52.89     -.8045 
    wb_18t24 |     8.948       8.15      3.194 |     8.727      6.572      4.082 
    wb_25t34 |     21.21      47.67      .3569 |     19.08      44.47      .7646 
    wb_35t44 |     16.97      13.25      .5533 |     16.39      13.52      .7694 
    wb_45t59 |     23.75      13.38       .129 |     24.88      14.52     .03651 
     avg_dur |     21.97      22.09      .4321 |     23.03      22.88       .616 
     hh_kids |     16.54      43.57      2.232 |     17.84      37.87      1.612 
mpreis_fla~t |     13.69      2.559      1.589 |     13.17      1.981      2.413 
  share_mail |     44.07      25.07     -.2602 |     41.93      23.13     -.2515 
turnout_to~q |     66.06      47.92     -.9538 |     65.54      57.53     -1.075 
     shr_spd |     .3683    .002105     -.3536 |     .3567    .002355     -.2541 
 street_dist |     .7015      .1035      1.234 |     .6756     .09848      1.442 


After:  _webal as the weighting variable

             |              Treat              |             Control             
             |      mean   variance   skewness |      mean   variance   skewness 
-------------+---------------------------------+---------------------------------
      ew_ges |      2362     133875      1.572 |      2362     133877      1.555 
    ew_biodt |     62.28      117.4     -.7574 |     62.28      117.4     -.6595 
   ew_dtmihi |     13.55      16.11      2.063 |     13.55      16.11      1.582 
    ew_ledig |     50.53      55.62      .3244 |     50.53      55.62      .3608 
  ew_married |     36.11      42.12   -.009557 |     36.11      42.12     .01203 
   wb_anteil |     63.92      63.42     -1.077 |     63.92      63.42     -.7795 
    wb_18t24 |     8.948       8.15      3.194 |     8.948       8.15      3.805 
    wb_25t34 |     21.21      47.67      .3569 |     21.21      47.67      .4127 
    wb_35t44 |     16.97      13.25      .5533 |     16.97      13.25      .7008 
    wb_45t59 |     23.75      13.38       .129 |     23.75      13.38      .2248 
     avg_dur |     21.97      22.09      .4321 |     21.97      22.09       .662 
     hh_kids |     16.54      43.57      2.232 |     16.54      43.57       2.05 
mpreis_fla~t |     13.69      2.559      1.589 |     13.69      2.559      2.097 
  share_mail |     44.07      25.07     -.2602 |     44.07      25.07      -.551 
turnout_to~q |     66.06      47.92     -.9538 |     66.06      47.93     -.9251 
     shr_spd |     .3683    .002105     -.3536 |     .3683    .002105     -.6417 
 street_dist |     .7015      .1035      1.234 |     .7015      .1035      1.809 

. 
.         grconst _webal, by(sb_new) fill
Note: there are by-groups where *_webal* is entirely missing.
(4256 real changes made)

.         
.         
.         
.  * 2) PSM: 1:1 matching (K:1 nearest neighbor) with propensity score matching (no replacement)
.         // generates several variables:
.         //      _weight:  frequency with which the obs is used as a match (here: 1 or . ), ids t
> reated and matched control units
.         // _id : "new" identifyer for all obs   
.         // _n1: ID of of nearest neighbor nr. 1
.         // _pdif: | pscore - ps of nearest neighbor | (essentially distance measure)
.         // _nn: #matched neighbors
.         
.         // compute propensity score, excluding units treated in 2013 (gen: pscore)
.         cap drop pscore

.         probit T $ebal if wahl_id==1 & Ei!=1, r 

Iteration 0:   log pseudolikelihood = -417.62289  
Iteration 1:   log pseudolikelihood = -392.30229  
Iteration 2:   log pseudolikelihood = -392.22165  
Iteration 3:   log pseudolikelihood = -392.22165  

Probit regression                                       Number of obs =    608
                                                        Wald chi2(17) =  45.62
                                                        Prob > chi2   = 0.0002
Log pseudolikelihood = -392.22165                       Pseudo R2     = 0.0608

-----------------------------------------------------------------------------------
                  |               Robust
                T | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
           ew_ges |   .0005318   .0003412     1.56   0.119    -.0001369    .0012005
         ew_biodt |   .0029844   .0570303     0.05   0.958    -.1087929    .1147617
        ew_dtmihi |   .0488718   .0648016     0.75   0.451    -.0781371    .1758806
         ew_ledig |  -.0703743   .0473281    -1.49   0.137    -.1631356     .022387
       ew_married |  -.0753891   .0413469    -1.82   0.068    -.1564276    .0056493
        wb_anteil |  -.0067874   .0638385    -0.11   0.915    -.1319086    .1183338
         wb_18t24 |   .0347578   .0430436     0.81   0.419    -.0496062    .1191218
         wb_25t34 |   .0091883   .0248669     0.37   0.712    -.0395499    .0579266
         wb_35t44 |   .0157599   .0374422     0.42   0.674    -.0576254    .0891451
         wb_45t59 |  -.0125979   .0246426    -0.51   0.609    -.0608966    .0357007
          avg_dur |  -.0071148   .0167648    -0.42   0.671    -.0399733    .0257437
          hh_kids |  -.0146988   .0362708    -0.41   0.685    -.0857881    .0563906
mpreis_flats_rent |   .0446348   .0447864     1.00   0.319    -.0431449    .1324144
       share_mail |    .034037   .0190186     1.79   0.074    -.0032389    .0713128
  turnout_tot_req |   .0504696   .0219305     2.30   0.021     .0074866    .0934525
          shr_spd |   2.263705   1.702983     1.33   0.184     -1.07408    5.601491
      street_dist |   .4799221   .2434437     1.97   0.049     .0027812    .9570629
            _cons |  -2.107595   3.329959    -0.63   0.527    -8.634196    4.419005
-----------------------------------------------------------------------------------

.         predict pscore if wahl_id==1 & Ei!=1
(option pr assumed; Pr(T))
(4,336 missing values generated)

.         
.         * define caliper
.         sum pscore

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      pscore |        608    .4438082    .1397595   .0476979   .9374577

.         local cal `r(sd)'/5

.         di `cal'
.0279519

.         * common support
.         sum pscore if T==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      pscore |        338    .4080973    .1333997   .0476979   .8730583

.         gen cmmn_smpl = pscore<=`r(max)'

.         return clear

.         sum pscore if T==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      pscore |        270    .4885129    .1348088   .1018099   .9374577

.         di `r(min)'
.10180987

.         replace cmmn_smpl = 0 if pscore<`r(min)'-0.00001
(1 real change made)

.         // pscore comes from regression above
.         psmatch2 T if wahl_id==1 &Ei!=1 & cmmn_smpl==1, pscore(pscore) noreplacement

. 
.         * smpl_psm: PSM matched sample
.         bys sb_new: egen smpl_psm = mean(_weight)
(656 missing values generated)

.         replace smpl_psm=0 if smpl_psm==.
(656 real changes made)

.         lab var smpl_psm "PSM matched sample id"

.         assert inlist(smpl_psm,0,1)

. 
.         
.         *drop generated variables from "psmatch2" to do mahalanobis matching next
.         drop _pscore _treated _support _weight _id _n1 _nn _pdif

. 
. * 3) Local matching (different frame since some control units are duplicated )
.         
.         // 1:k matching without replacement local_matching_sample_1_within_district
.         merge 1:1 wahl_id sb_new using "$tmp/local_matching_sample_1_within_district.dta", asser
> t(1 3) nogen 
(variable wahl_id was byte, now float to accommodate using data's values)

    Result                      Number of obs
    -----------------------------------------
    Not matched                           936
        from master                       936  
        from using                          0  

    Matched                             4,008  
    -----------------------------------------

.         
.         // new frame for 1:k matching with replacement (several controls to one treated unit)
.         frame copy default frlm, replace 
(note: frame frlm not found)

.         frame frlm: merge 1:m sb_new wahl_id using "$tmp/local_matching_sample_k.dta", assert(1 
> 3) nogen 
(variable stadtbez was byte, now float to accommodate using data's values)

    Result                      Number of obs
    -----------------------------------------
    Not matched                           696
        from master                       696  
        from using                          0  

    Matched                             7,248  
    -----------------------------------------

.         frame frlm: bys sb_new wahl_id: gen lm_unique = (_n==1) // ids unique controls 

. 
. * 4) Mahalanobis 1 NN matching with replacement
. *       // note: untreated units matched to multiple treated units are duplicated
. *               + TW clustering following Colmer et al (Restud, 2024) 
. *       // note: matching w/o replacement is only implemented with 1:1 PSM
.         psmatch2 T if wahl_id==1 &Ei!=1,  mahalanobis($ebal) 

.         
.         * smpl_maha : mahanabolis matched sample
.         cap drop tmp*

.         bys sb_new: egen tmp = mean(_weight)
(1,544 missing values generated)

.         gen smpl_maha = !missing(tmp)

.         assert inlist(smpl_maha,0,1)

.         
.         *generate ID of matched untreated precincts
.         gen     mm_ctr_id = _n1
(4,674 missing values generated)

.         replace mm_ctr_id = _id if mm_ctr_id==. & _weight!=.
(155 real changes made)

.         grconst mm_ctr_id, by(sb_new) fill      
Note: there are by-groups where *mm_ctr_id* is entirely missing.
(2975 real changes made)

.         lab var mm_ctr_id "MM: untreated unit ID"               

. 
. 
. * Gen pair match identifyer, duplicate multiple matched untreated units
. frame copy default tmp, replace 

. frame tmp{
.         keep if wahl_id==1
(4,326 observations deleted)
.         
.         *generate duplicates for all control precincts matched more than once
.         expand _weight
(193 missing counts ignored; observations not deleted)
(115 observations created)
.         
.         *tmp_merger is a variable used for the merge
.         bys sb_new: gen tmp_merge=_n
.         
.         *gen matched pari ID twc3 is matched pairs ID
.         bys     mm_ctr_id _treated: gen tmp1 = mm_ctr_id + _n
(193 missing values generated)
.         egen    mm_pair_id = group(mm_ctr_id tmp1)
(193 missing values generated)
.         lab var mm_pair_id "MM: matched pair ID"
.         
.         keep sb_new tmp_merge mm_pair_id
.         
.         tempfile maha_matches 
.         save `maha_matches'
file C:\Users\Alipour\AppData\Local\Temp\9\ST_77c8_000002.tmp saved as .dta format
. }       

. 
. 
.  * gen new frame for mahanabolis matched sample (duplicate units)       
. frame copy default frmaha, replace
(note: frame frmaha not found)

. frame frmaha{
.                 
.         *gen duplicates of all control precincts matched more than once (also for the whole samp
> le)
.         grconst _weight, by(sb_new) fill        
Note: there are by-groups where *_weight* is entirely missing.
(2975 real changes made)
.         expand _weight
(1,544 missing counts ignored; observations not deleted)
(920 observations created)
.         bys sb_new wahl_id: gen tmp_merge=_n
. 
.         merge m:1 sb_new tmp_merge using `maha_matches', keepusing(mm_pair_id) assert(3)

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                             5,864  (_merge==3)
    -----------------------------------------
.         
.         bys mm_pair_id: assert _N==16 if smpl_maha==1
. 
. }       

. 
. ********************************************************************************
.                 // Matching: Baseline Specification (Figure C3 //
. ********************************************************************************        
. 
.         
.                 estimates clear

.         
.         // Mahalanobis distance matching
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {             
  2.                 // untreat units matched to several treated duplicates + TW cluster by pairma
> tch and untreated unit     
.                 frame frmaha: reghdfe `v' F7event-L7event F1event $ctr [aw=wahlber_gesamt] if sm
> pl_trim==1 & smpl_maha==1 & Ei!=1, ///
>                          absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(mm_pair_id mm_ctr_id#i.wa
> hl_id)
  3.                 
.                 estimates store `v'_maha
  4.         }
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,065
Absorbing 2 HDFE groups                           F(  26,    269) =      11.41
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9728
                                                  Adj R-squared   =     0.9679
Number of clusters (mm_pair_id) =        270      Within R-sq.    =     0.1589
Number of clusters (mm_ctr_id#wahl_id) =      1,240Root MSE       =     1.6008

              (Std. err. adjusted for 270 clusters in mm_pair_id mm_ctr_id#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0204644   .3639733     0.06   0.955    -.6961342     .737063
          F6event |   .2116473   .3416698     0.62   0.536    -.4610398    .8843344
          F5event |   .3297091   .2719107     1.21   0.226    -.2056346    .8650529
          F4event |   .1052881   .1829172     0.58   0.565    -.2548433    .4654196
          F3event |   .0311777   .2005435     0.16   0.877    -.3636568    .4260122
          F2event |   .0643422   .1411065     0.46   0.649    -.2134713    .3421558
          L0event |  -.9016919   .2565459    -3.51   0.001    -1.406785   -.3965987
          L1event |  -.7269593   .2727998    -2.66   0.008    -1.264053   -.1898651
          L2event |  -.6918789   .3006628    -2.30   0.022     -1.28383   -.0999274
          L3event |  -.2165276   .3193798    -0.68   0.498    -.8453297    .4122744
          L4event |  -1.037013   .9000122    -1.15   0.250    -2.808977    .7349505
          L5event |  -.9137504   1.059082    -0.86   0.389    -2.998894    1.171393
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |  -2.168958   1.411314    -1.54   0.126    -4.947584    .6096684
         ew_biodt |   .3211834   .0413653     7.76   0.000     .2397424    .4026243
        ew_dtmihi |   .0037517   .0671352     0.06   0.955    -.1284256     .135929
         ew_ledig |   .2518463   .0741249     3.40   0.001     .1059076    .3977849
       ew_married |    .433533   .0723812     5.99   0.000     .2910273    .5760387
        wb_anteil |  -.2727654   .0324522    -8.41   0.000    -.3366581   -.2088727
          wb_ausl |    .004548   .0207828     0.22   0.827    -.0363697    .0454657
         wb_18t24 |  -.0166134   .0360199    -0.46   0.645    -.0875302    .0543033
         wb_25t34 |  -.1188192   .0283571    -4.19   0.000    -.1746492   -.0629892
         wb_35t44 |   .0020769   .0325032     0.06   0.949    -.0619162    .0660699
         wb_45t59 |  -.0169703   .0278866    -0.61   0.543    -.0718742    .0379335
          avg_dur |  -.0311813   .0296989    -1.05   0.295     -.089653    .0272905
          hh_kids |  -.0409481   .0549456    -0.75   0.457    -.1491263      .06723
mpreis_flats_rent |   .0151131   .0290991     0.52   0.604    -.0421779    .0724041
            _cons |   25.11554   12.74867     1.97   0.050     .0156757    50.21541
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           0         200     |
             sb_new |       425          25         400     |
------------------------------------------------------------+
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,065
Absorbing 2 HDFE groups                           F(  26,    269) =      14.78
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9646
                                                  Adj R-squared   =     0.9581
Number of clusters (mm_pair_id) =        270      Within R-sq.    =     0.2113
Number of clusters (mm_ctr_id#wahl_id) =      1,240Root MSE       =     1.5969

              (Std. err. adjusted for 270 clusters in mm_pair_id mm_ctr_id#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .1076793   .3610074     0.30   0.766      -.60308    .8184387
          F6event |   .1590819   .2924271     0.54   0.587    -.4166551    .7348189
          F5event |  -.3675662   .2948201    -1.25   0.214    -.9480146    .2128822
          F4event |  -.2302014   .1752106    -1.31   0.190    -.5751598    .1147571
          F3event |  -.0058325   .1651338    -0.04   0.972    -.3309516    .3192867
          F2event |   .0154443   .1569564     0.10   0.922    -.2935749    .3244635
          L0event |    .535943   .2358788     2.27   0.024     .0715395    1.000346
          L1event |   .7996496    .255023     3.14   0.002     .2975548    1.301744
          L2event |   1.018302   .3017393     3.37   0.001     .4242312    1.612373
          L3event |   .2927409    .284429     1.03   0.304    -.2672493     .852731
          L4event |   2.561839   .8574762     2.99   0.003     .8736215    4.250057
          L5event |   2.291039   .9397293     2.44   0.015     .4408796    4.141199
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   3.497545    1.53332     2.28   0.023     .4787109    6.516379
         ew_biodt |   .4201087   .0379941    11.06   0.000      .345305    .4949124
        ew_dtmihi |  -.2142547   .0707549    -3.03   0.003    -.3535584   -.0749509
         ew_ledig |   .2122281   .0981054     2.16   0.031     .0190761    .4053802
       ew_married |   .2010665   .0959565     2.10   0.037     .0121454    .3899877
        wb_anteil |  -.2535629   .0326161    -7.77   0.000    -.3177782   -.1893475
          wb_ausl |  -.0812231   .0186675    -4.35   0.000    -.1179761   -.0444701
         wb_18t24 |   .0024306   .0365342     0.07   0.947    -.0694986    .0743598
         wb_25t34 |    .098373   .0277295     3.55   0.000     .0437785    .1529675
         wb_35t44 |   .0098319   .0341413     0.29   0.774    -.0573862      .07705
         wb_45t59 |  -.0004213   .0268999    -0.02   0.988    -.0533825    .0525399
          avg_dur |   .0641169   .0320366     2.00   0.046     .0010425    .1271913
          hh_kids |  -.0896017   .0494773    -1.81   0.071    -.1870137    .0078102
mpreis_flats_rent |  -.0025297   .0291624    -0.09   0.931    -.0599453    .0548858
            _cons |  -23.19376    12.7554    -1.82   0.070    -48.30687    1.919341
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           0         200     |
             sb_new |       425          25         400     |
------------------------------------------------------------+
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,065
Absorbing 2 HDFE groups                           F(  26,    269) =      29.49
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9915
                                                  Adj R-squared   =     0.9899
Number of clusters (mm_pair_id) =        270      Within R-sq.    =     0.4412
Number of clusters (mm_ctr_id#wahl_id) =      1,240Root MSE       =     1.4879

              (Std. err. adjusted for 270 clusters in mm_pair_id mm_ctr_id#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .1281437     .38159     0.34   0.737    -.6231391    .8794264
          F6event |    .370728   .3137669     1.18   0.238    -.2470231    .9884792
          F5event |  -.0378568   .3136335    -0.12   0.904    -.6553453    .5796317
          F4event |  -.1249128   .1837177    -0.68   0.497    -.4866203    .2367947
          F3event |   .0253449   .1801976     0.14   0.888     -.329432    .3801219
          F2event |    .079787    .157821     0.51   0.614    -.2309344    .3905084
          L0event |  -.3657485   .1752616    -2.09   0.038    -.7108073   -.0206897
          L1event |   .0726904   .1980712     0.37   0.714    -.3172765    .4626572
          L2event |   .3264236   .2463764     1.32   0.186    -.1586476    .8114948
          L3event |   .0762138   .2920175     0.26   0.794    -.4987167    .6511444
          L4event |   1.524827   .5424051     2.81   0.005     .4569277    2.592726
          L5event |   1.377291   .6685484     2.06   0.040     .0610379    2.693543
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   1.328586   1.504422     0.88   0.378    -1.633352    4.290524
         ew_biodt |    .741292   .0392731    18.88   0.000     .6639703    .8186138
        ew_dtmihi |  -.2105029   .0705844    -2.98   0.003     -.349471   -.0715348
         ew_ledig |   .4640745   .0842762     5.51   0.000     .2981497    .6299993
       ew_married |   .6345997   .0812095     7.81   0.000     .4747127    .7944866
        wb_anteil |  -.5263282   .0332084   -15.85   0.000    -.5917097   -.4609468
          wb_ausl |  -.0766751    .018251    -4.20   0.000    -.1126082   -.0407421
         wb_18t24 |  -.0141829   .0313572    -0.45   0.651    -.0759196    .0475539
         wb_25t34 |  -.0204462   .0213126    -0.96   0.338     -.062407    .0215146
         wb_35t44 |   .0119088   .0266563     0.45   0.655    -.0405726    .0643903
         wb_45t59 |  -.0173917    .024863    -0.70   0.485    -.0663424    .0315591
          avg_dur |   .0329356   .0303941     1.08   0.280     -.026905    .0927762
          hh_kids |  -.1305499   .0443697    -2.94   0.004    -.2179058   -.0431939
mpreis_flats_rent |   .0125834   .0279345     0.45   0.653    -.0424146    .0675814
            _cons |   1.921777   12.95634     0.15   0.882    -23.58695     27.4305
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           0         200     |
             sb_new |       425          25         400     |
------------------------------------------------------------+

. 
.         // Propensity score matching
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                  reghdfe `v' F7event-L7event F1event $ctr [aw=wahlber_gesamt] if smpl_trim==1
>  & smpl_psm==1 & Ei!=1, ///
>                         absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
  3.                 
.                 estimates store `v'_psm
  4.         }
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,036
Absorbing 2 HDFE groups                           F(  26,    535) =      14.31
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9711
                                                  Adj R-squared   =     0.9644
                                                  Within R-sq.    =     0.1615
Number of clusters (sb_new)  =        536         Root MSE        =     1.6943

                                    (Std. err. adjusted for 536 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0741139   .3749706     0.20   0.843    -.6624813    .8107091
          F6event |   .3029559   .3357806     0.90   0.367    -.3566542     .962566
          F5event |   .3430369   .2649075     1.29   0.196    -.1773495    .8634233
          F4event |   .1113783   .1805409     0.62   0.538    -.2432776    .4660342
          F3event |   .0115321   .1753473     0.07   0.948    -.3329216    .3559857
          F2event |   .0384424   .1255756     0.31   0.760    -.2082393    .2851241
          L0event |  -1.034006   .2376532    -4.35   0.000    -1.500854   -.5671582
          L1event |  -.8965533   .2377946    -3.77   0.000    -1.363679   -.4294277
          L2event |  -.7761962   .2660425    -2.92   0.004    -1.298812   -.2535803
          L3event |  -.3541565   .2793231    -1.27   0.205     -.902861     .194548
          L4event |  -.9370032   .6699022    -1.40   0.162    -2.252964     .378958
          L5event |  -.7797152   .9428527    -0.83   0.409    -2.631863    1.072432
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |  -.5918891   1.033145    -0.57   0.567    -2.621408     1.43763
         ew_biodt |   .3527407   .0323566    10.90   0.000     .2891791    .4163022
        ew_dtmihi |   .0244167   .0560066     0.44   0.663    -.0856031    .1344366
         ew_ledig |   .1982854   .0617487     3.21   0.001     .0769857     .319585
       ew_married |   .4330921   .0646804     6.70   0.000     .3060334    .5601507
        wb_anteil |  -.2697055   .0237098   -11.38   0.000    -.3162813   -.2231297
          wb_ausl |   .0011269   .0150106     0.08   0.940    -.0283601    .0306139
         wb_18t24 |   -.031333   .0317104    -0.99   0.324    -.0936252    .0309592
         wb_25t34 |  -.0691719   .0202066    -3.42   0.001    -.1088659   -.0294778
         wb_35t44 |   .0023602   .0245103     0.10   0.923     -.045788    .0505085
         wb_45t59 |   .0013929   .0237327     0.06   0.953    -.0452277    .0480136
          avg_dur |  -.0312726   .0242216    -1.29   0.197    -.0788538    .0163085
          hh_kids |  -.0487789   .0453479    -1.08   0.283    -.1378606    .0403028
mpreis_flats_rent |   .0273465   .0258153     1.06   0.290    -.0233652    .0780583
            _cons |   11.67994   9.766261     1.20   0.232    -7.504976    30.86487
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       536         536           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,036
Absorbing 2 HDFE groups                           F(  26,    535) =      15.31
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9625
                                                  Adj R-squared   =     0.9538
                                                  Within R-sq.    =     0.1994
Number of clusters (sb_new)  =        536         Root MSE        =     1.6675

                                    (Std. err. adjusted for 536 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1321892     .33867    -0.39   0.696    -.7974751    .5330968
          F6event |   .0021243   .2692801     0.01   0.994    -.5268518    .5311004
          F5event |   -.572753   .2643386    -2.17   0.031    -1.092022   -.0534842
          F4event |  -.2880452   .1670489    -1.72   0.085    -.6161974     .040107
          F3event |  -.0655228   .1582651    -0.41   0.679    -.3764201    .2453744
          F2event |  -.0642778   .1283461    -0.50   0.617    -.3164019    .1878464
          L0event |   .6476426   .2208854     2.93   0.004     .2137336    1.081552
          L1event |   .9515512   .2291784     4.15   0.000     .5013513    1.401751
          L2event |   1.120309    .272789     4.11   0.000     .5844406    1.656178
          L3event |   .6207417   .2711419     2.29   0.022     .0881085    1.153375
          L4event |   2.361028   .6529275     3.62   0.000     1.078412    3.643645
          L5event |   2.857763   .7463764     3.83   0.000     1.391575    4.323951
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   2.034515   1.419224     1.43   0.152    -.7534195     4.82245
         ew_biodt |   .3979659   .0333944    11.92   0.000     .3323658    .4635661
        ew_dtmihi |  -.2050249   .0596715    -3.44   0.001    -.3222442   -.0878057
         ew_ledig |   .2354295   .0876105     2.69   0.007     .0633268    .4075323
       ew_married |   .2489588   .0860226     2.89   0.004     .0799754    .4179422
        wb_anteil |  -.2601322   .0257798   -10.09   0.000    -.3107743   -.2094901
          wb_ausl |  -.0702112   .0145291    -4.83   0.000    -.0987523   -.0416701
         wb_18t24 |  -.0146553   .0302395    -0.48   0.628    -.0740579    .0447474
         wb_25t34 |   .0501086    .020095     2.49   0.013     .0106339    .0895834
         wb_35t44 |   .0042466   .0265875     0.16   0.873     -.047982    .0564753
         wb_45t59 |    -.01683   .0221032    -0.76   0.447    -.0602498    .0265898
          avg_dur |   .0518852   .0265635     1.95   0.051    -.0002964    .1040668
          hh_kids |  -.0941021   .0438144    -2.15   0.032    -.1801715   -.0080328
mpreis_flats_rent |  -.0146246   .0245007    -0.60   0.551    -.0627539    .0335048
            _cons |  -11.03077   12.04184    -0.92   0.360    -34.68586    12.62433
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       536         536           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,036
Absorbing 2 HDFE groups                           F(  26,    535) =      45.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9907
                                                  Adj R-squared   =     0.9886
                                                  Within R-sq.    =     0.4387
Number of clusters (sb_new)  =        536         Root MSE        =     1.5805

                                    (Std. err. adjusted for 536 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0580755   .3428658    -0.17   0.866    -.7316038    .6154528
          F6event |   .3050795   .2903905     1.05   0.294    -.2653659    .8755249
          F5event |  -.2297158   .2700381    -0.85   0.395    -.7601809    .3007493
          F4event |  -.1766666   .1713585    -1.03   0.303    -.5132847    .1599515
          F3event |   -.053991   .1625173    -0.33   0.740    -.3732413    .2652594
          F2event |   -.025835   .1379889    -0.19   0.852    -.2969014    .2452315
          L0event |  -.3863628   .1655614    -2.33   0.020    -.7115929   -.0611328
          L1event |   .0549982   .2053447     0.27   0.789    -.3483826     .458379
          L2event |   .3441136    .232419     1.48   0.139    -.1124522    .8006795
          L3event |   .2665857   .2491031     1.07   0.285    -.2227544    .7559257
          L4event |   1.424026   .5263623     2.71   0.007     .3900354    2.458016
          L5event |    2.07805    .993678     2.09   0.037     .1260605    4.030038
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   1.442627   1.119956     1.29   0.198    -.7574243    3.642678
         ew_biodt |   .7507066   .0344884    21.77   0.000     .6829574    .8184558
        ew_dtmihi |   -.180608   .0565386    -3.19   0.001    -.2916729   -.0695432
         ew_ledig |   .4337151   .0753785     5.75   0.000      .285641    .5817892
       ew_married |   .6820509   .0730554     9.34   0.000     .5385404    .8255615
        wb_anteil |  -.5298377   .0268341   -19.74   0.000    -.5825509   -.4771245
          wb_ausl |  -.0690843   .0156497    -4.41   0.000    -.0998267   -.0383419
         wb_18t24 |  -.0459883   .0270951    -1.70   0.090    -.0992141    .0072374
         wb_25t34 |  -.0190632   .0171336    -1.11   0.266    -.0527206    .0145941
         wb_35t44 |   .0066069   .0214441     0.31   0.758    -.0355181    .0487319
         wb_45t59 |  -.0154371   .0210239    -0.73   0.463    -.0567365    .0258624
          avg_dur |   .0206126   .0255772     0.81   0.421    -.0296315    .0708567
          hh_kids |  -.1428811   .0370606    -3.86   0.000    -.2156833    -.070079
mpreis_flats_rent |    .012722   .0247531     0.51   0.607    -.0359031    .0613471
            _cons |   .6491592   10.45133     0.06   0.950    -19.88151    21.17983
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       536         536           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         // Entropy balancing
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                  reghdfe `v' F7event-L7event F1event $ctr [aw=_webal] if smpl_trim==1 & Ei!=1
> , absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
  3.                 
.                 estimates store `v'_ebal
  4.         }               
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 2 HDFE groups                           F(  26,    607) =      13.86
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9694
                                                  Adj R-squared   =     0.9627
                                                  Within R-sq.    =     0.1490
Number of clusters (sb_new)  =        608         Root MSE        =     1.6927

                                    (Std. err. adjusted for 608 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0087383   .3713533    -0.02   0.981    -.7380315    .7205549
          F6event |   .2576084   .3356919     0.77   0.443    -.4016502     .916867
          F5event |   .3274991   .2651166     1.24   0.217     -.193158    .8481562
          F4event |   .0976001   .1787303     0.55   0.585    -.2534048    .4486049
          F3event |   .0360483   .1753832     0.21   0.837    -.3083833    .3804799
          F2event |   .0409597   .1261398     0.32   0.746    -.2067637    .2886831
          L0event |     -.9946   .2384474    -4.17   0.000    -1.462882   -.5263179
          L1event |  -.7907408   .2350774    -3.36   0.001    -1.252405    -.329077
          L2event |  -.7215743   .2617238    -2.76   0.006    -1.235568   -.2075803
          L3event |  -.2586391   .2880571    -0.90   0.370    -.8243486    .3070704
          L4event |  -.8203667   .6644681    -1.23   0.217    -2.125302    .4845688
          L5event |  -.7034094   .9161896    -0.77   0.443    -2.502696    1.095877
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |  -.7794942    1.07164    -0.73   0.467    -2.884067    1.325079
         ew_biodt |   .3493499   .0314544    11.11   0.000     .2875772    .4111225
        ew_dtmihi |   .0426724   .0566086     0.75   0.451       -.0685    .1538449
         ew_ledig |   .2007029   .0640133     3.14   0.002     .0749885    .3264172
       ew_married |   .4178402   .0672649     6.21   0.000       .28574    .5499404
        wb_anteil |  -.2765998   .0227474   -12.16   0.000    -.3212729   -.2319267
          wb_ausl |   .0055747   .0155484     0.36   0.720    -.0249605    .0361099
         wb_18t24 |   -.026356   .0311189    -0.85   0.397    -.0874698    .0347577
         wb_25t34 |  -.0764783   .0221196    -3.46   0.001    -.1199184   -.0330381
         wb_35t44 |   .0006804    .026039     0.03   0.979     -.050457    .0518179
         wb_45t59 |   .0074935    .024468     0.31   0.760    -.0405587    .0555458
          avg_dur |  -.0420762   .0243739    -1.73   0.085    -.0899436    .0057912
          hh_kids |  -.0344123   .0430947    -0.80   0.425     -.119045    .0502205
mpreis_flats_rent |   .0285152   .0260118     1.10   0.273     -.022569    .0795993
            _cons |   14.13966   10.23495     1.38   0.168    -5.960548    34.23986
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       608         608           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 2 HDFE groups                           F(  26,    607) =      15.35
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9632
                                                  Adj R-squared   =     0.9550
                                                  Within R-sq.    =     0.1835
Number of clusters (sb_new)  =        608         Root MSE        =     1.6574

                                    (Std. err. adjusted for 608 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0744955   .3409173    -0.22   0.827    -.7440161    .5950251
          F6event |   .0280484   .2672182     0.10   0.916    -.4967361    .5528328
          F5event |  -.4680806   .2655675    -1.76   0.078    -.9896233    .0534622
          F4event |   -.253949    .165897    -1.53   0.126    -.5797507    .0718527
          F3event |  -.0371691   .1567259    -0.24   0.813    -.3449599    .2706218
          F2event |   .0004603   .1308606     0.00   0.997    -.2565343    .2574548
          L0event |   .6735219   .2199317     3.06   0.002     .2416024    1.105441
          L1event |   .9090775   .2252613     4.04   0.000     .4666914    1.351464
          L2event |   1.082976   .2677113     4.05   0.000      .557223    1.608729
          L3event |   .6017731   .2766336     2.18   0.030      .058498    1.145048
          L4event |   2.272342   .6304145     3.60   0.000     1.034284    3.510401
          L5event |   2.537635   .7880855     3.22   0.001     .9899301     4.08534
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   1.535559   1.328996     1.16   0.248    -1.074428    4.145547
         ew_biodt |   .3869464   .0344572    11.23   0.000     .3192766    .4546162
        ew_dtmihi |  -.2221353   .0593278    -3.74   0.000    -.3386479   -.1056227
         ew_ledig |   .1951869   .0894045     2.18   0.029     .0196072    .3707666
       ew_married |   .2136473   .0863069     2.48   0.014     .0441509    .3831437
        wb_anteil |  -.2691468   .0288581    -9.33   0.000    -.3258207   -.2124729
          wb_ausl |   -.057801   .0138478    -4.17   0.000    -.0849964   -.0306056
         wb_18t24 |  -.0231422   .0294375    -0.79   0.432    -.0809539    .0346696
         wb_25t34 |   .0620507   .0200192     3.10   0.002     .0227354     .101366
         wb_35t44 |    .003697   .0266977     0.14   0.890    -.0487341    .0561282
         wb_45t59 |  -.0391679   .0216772    -1.81   0.071    -.0817393    .0034035
          avg_dur |    .054763   .0266748     2.05   0.041      .002377     .107149
          hh_kids |  -.0858305   .0438721    -1.96   0.051      -.17199     .000329
mpreis_flats_rent |  -.0193058   .0232239    -0.83   0.406    -.0649148    .0263032
            _cons |  -1.781593   12.09983    -0.15   0.883     -25.5442    21.98101
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       608         608           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 2 HDFE groups                           F(  26,    607) =      40.86
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9906
                                                  Adj R-squared   =     0.9885
                                                  Within R-sq.    =     0.4102
Number of clusters (sb_new)  =        608         Root MSE        =     1.5624

                                    (Std. err. adjusted for 608 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   -.083234   .3457743    -0.24   0.810    -.7622931    .5958251
          F6event |   .2856561   .2938069     0.97   0.331    -.2913454    .8626575
          F5event |   -.140581   .2718467    -0.52   0.605    -.6744553    .3932932
          F4event |  -.1563485   .1771545    -0.88   0.378    -.5042587    .1915616
          F3event |   -.001121   .1617318    -0.01   0.994    -.3187428    .3165009
          F2event |   .0414204   .1363707     0.30   0.761    -.2263953    .3092361
          L0event |  -.3210776   .1637456    -1.96   0.050    -.6426543     .000499
          L1event |    .118337   .1998226     0.59   0.554    -.2740907    .5107646
          L2event |   .3614021   .2384903     1.52   0.130    -.1069642    .8297684
          L3event |   .3431347   .2392407     1.43   0.152    -.1267053    .8129748
          L4event |   1.451976   .4780986     3.04   0.002      .513048    2.390904
          L5event |   1.834227   .9816091     1.87   0.062    -.0935353    3.761989
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   .7560662    1.18583     0.64   0.524    -1.572761    3.084893
         ew_biodt |   .7362963   .0327754    22.46   0.000     .6719294    .8006631
        ew_dtmihi |  -.1794627   .0556777    -3.22   0.001     -.288807   -.0701185
         ew_ledig |   .3958899   .0764701     5.18   0.000     .2457119     .546068
       ew_married |   .6314876    .074273     8.50   0.000     .4856243     .777351
        wb_anteil |  -.5457466   .0273057   -19.99   0.000    -.5993718   -.4921214
          wb_ausl |  -.0522263   .0153706    -3.40   0.001    -.0824123   -.0220403
         wb_18t24 |  -.0494983   .0273705    -1.81   0.071    -.1032507    .0042541
         wb_25t34 |  -.0144276   .0172102    -0.84   0.402    -.0482264    .0193712
         wb_35t44 |   .0043775   .0224682     0.19   0.846    -.0397473    .0485023
         wb_45t59 |  -.0316744   .0214629    -1.48   0.141     -.073825    .0104762
          avg_dur |   .0126868   .0253868     0.50   0.617    -.0371699    .0625435
          hh_kids |  -.1202428   .0392563    -3.06   0.002    -.1973375   -.0431482
mpreis_flats_rent |   .0092094   .0253148     0.36   0.716    -.0405058    .0589246
            _cons |   12.35805   11.16568     1.11   0.269    -9.570009     34.2861
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       608         608           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         // Local matching 
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                  reghdfe `v' F7event-L7event F1event $ctr [aw=wahlber_gesamt] if smpl_trim==1
>  & smpl_lmatch==1 &Ei!=1, ///
>                         absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
  3.                 estimates store `v'_lm
  4.         }
(MWFE estimator converged in 7 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,734
Absorbing 2 HDFE groups                           F(  26,    492) =      14.97
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9719
                                                  Adj R-squared   =     0.9652
                                                  Within R-sq.    =     0.1721
Number of clusters (sb_new)  =        493         Root MSE        =     1.6999

                                    (Std. err. adjusted for 493 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0606406   .3888122    -0.16   0.876    -.8245778    .7032966
          F6event |   .1924839   .3392042     0.57   0.571    -.4739837    .8589514
          F5event |   .3147644    .274919     1.14   0.253    -.2253957    .8549245
          F4event |  -.0116251   .1831246    -0.06   0.949    -.3714277    .3481776
          F3event |   .0087488   .1833859     0.05   0.962    -.3515673     .369065
          F2event |   .0424017   .1317467     0.32   0.748    -.2164539    .3012572
          L0event |  -1.058378   .2520214    -4.20   0.000    -1.553549   -.5632074
          L1event |   -1.01375   .2467935    -4.11   0.000    -1.498649   -.5288509
          L2event |  -.7761332   .2775693    -2.80   0.005    -1.321501   -.2307657
          L3event |  -.2378137   .2926587    -0.81   0.417    -.8128286    .3372013
          L4event |  -1.088532   .7621094    -1.43   0.154    -2.585923    .4088582
          L5event |  -.9624943    1.10973    -0.87   0.386    -3.142888    1.217899
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |  -.8348178   1.084727    -0.77   0.442    -2.966087    1.296451
         ew_biodt |   .3798799    .032505    11.69   0.000     .3160141    .4437457
        ew_dtmihi |   .1084285   .0560195     1.94   0.053    -.0016384    .2184955
         ew_ledig |   .1927115   .0669714     2.88   0.004     .0611262    .3242968
       ew_married |   .4093649   .0682324     6.00   0.000     .2753021    .5434277
        wb_anteil |  -.2840705   .0244481   -11.62   0.000    -.3321061    -.236035
          wb_ausl |  -.0009003   .0166128    -0.05   0.957    -.0335411    .0317405
         wb_18t24 |  -.0015923   .0358536    -0.04   0.965    -.0720373    .0688527
         wb_25t34 |  -.0705281   .0213232    -3.31   0.001    -.1124239   -.0286324
         wb_35t44 |   -.005388   .0256475    -0.21   0.834      -.05578    .0450041
         wb_45t59 |   .0083793   .0249242     0.34   0.737    -.0405917    .0573504
          avg_dur |  -.0388968   .0229937    -1.69   0.091    -.0840747    .0062812
          hh_kids |  -.0412349   .0461736    -0.89   0.372    -.1319567     .049487
mpreis_flats_rent |   .0086312   .0278056     0.31   0.756    -.0460011    .0632635
            _cons |   12.95828   10.20436     1.27   0.205     -7.09122    33.00777
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       493         493           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 7 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,734
Absorbing 2 HDFE groups                           F(  26,    492) =      14.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9621
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2056
Number of clusters (sb_new)  =        493         Root MSE        =     1.6753

                                    (Std. err. adjusted for 493 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .2320696   .3478634     0.67   0.505    -.4514114    .9155506
          F6event |   .2929666   .2736385     1.07   0.285    -.2446777    .8306109
          F5event |  -.2982652   .2779031    -1.07   0.284    -.8442885    .2477582
          F4event |  -.1314575   .1722406    -0.76   0.446    -.4698753    .2069603
          F3event |     .01057   .1614937     0.07   0.948    -.3067324    .3278725
          F2event |  -.0319204    .132866    -0.24   0.810    -.2929753    .2291344
          L0event |   .6673726   .2322732     2.87   0.004     .2110029    1.123742
          L1event |   .9538544   .2361271     4.04   0.000     .4899124    1.417796
          L2event |   1.111582   .2813223     3.95   0.000     .5588409    1.664323
          L3event |   .5437925   .2805302     1.94   0.053    -.0073924    1.094977
          L4event |   2.561083   .7559283     3.39   0.001     1.075838    4.046329
          L5event |   3.223315    .779724     4.13   0.000     1.691316    4.755315
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   1.999014   1.488306     1.34   0.180    -.9252052    4.923234
         ew_biodt |   .3602801   .0338983    10.63   0.000     .2936769    .4268834
        ew_dtmihi |  -.2695063   .0656236    -4.11   0.000    -.3984433   -.1405693
         ew_ledig |   .1966164   .0874206     2.25   0.025     .0248527    .3683802
       ew_married |   .2183334   .0865977     2.52   0.012     .0481865    .3884803
        wb_anteil |  -.2278282   .0266006    -8.56   0.000     -.280093   -.1755634
          wb_ausl |  -.0800666   .0155732    -5.14   0.000    -.1106647   -.0494684
         wb_18t24 |  -.0384197   .0331343    -1.16   0.247    -.1035218    .0266824
         wb_25t34 |   .0579832   .0214782     2.70   0.007      .015783    .1001834
         wb_35t44 |   .0237376   .0278307     0.85   0.394    -.0309441    .0784192
         wb_45t59 |  -.0380373   .0218462    -1.74   0.082    -.0809606    .0048861
          avg_dur |   .0455383   .0249099     1.83   0.068    -.0034045    .0944812
          hh_kids |  -.0681452    .046327    -1.47   0.142    -.1591683    .0228778
mpreis_flats_rent |  -.0157209   .0266375    -0.59   0.555    -.0680582    .0366165
            _cons |  -6.783828   12.42457    -0.55   0.585     -31.1956    17.62794
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       493         493           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 7 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,734
Absorbing 2 HDFE groups                           F(  26,    492) =      39.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9906
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4413
Number of clusters (sb_new)  =        493         Root MSE        =     1.5975

                                    (Std. err. adjusted for 493 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .1714288   .3318704     0.52   0.606    -.4806293     .823487
          F6event |   .4854498   .2851519     1.70   0.089    -.0748159    1.045716
          F5event |   .0164997    .276998     0.06   0.953    -.5277453    .5607446
          F4event |  -.1430821   .1815457    -0.79   0.431    -.4997827    .2136185
          F3event |   .0193186   .1755772     0.11   0.912    -.3256551    .3642923
          F2event |   .0104818   .1471854     0.07   0.943    -.2787077    .2996713
          L0event |  -.3910052   .1751272    -2.23   0.026    -.7350946   -.0469158
          L1event |  -.0598955   .2183373    -0.27   0.784     -.488884     .369093
          L2event |   .3354492   .2479893     1.35   0.177    -.1517996    .8226979
          L3event |   .3059793   .2544678     1.20   0.230    -.1939984     .805957
          L4event |   1.472552   .5494017     2.68   0.008     .3930887    2.552015
          L5event |   2.260822   1.134863     1.99   0.047     .0310459    4.490598
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   1.164197   1.116092     1.04   0.297    -1.028698    3.357092
         ew_biodt |   .7401601   .0349207    21.20   0.000      .671548    .8087721
        ew_dtmihi |  -.1610776   .0588976    -2.73   0.006    -.2767995   -.0453558
         ew_ledig |   .3893281   .0742546     5.24   0.000     .2434328    .5352234
       ew_married |   .6276984    .073015     8.60   0.000     .4842387    .7711581
        wb_anteil |  -.5118988   .0292251   -17.52   0.000    -.5693203   -.4544773
          wb_ausl |  -.0809669   .0164167    -4.93   0.000    -.1132223   -.0487114
         wb_18t24 |   -.040012   .0310655    -1.29   0.198    -.1010494    .0210254
         wb_25t34 |   -.012545   .0185645    -0.68   0.500    -.0490204    .0239305
         wb_35t44 |   .0183496   .0238435     0.77   0.442     -.028498    .0651972
         wb_45t59 |  -.0296579   .0215216    -1.38   0.169    -.0719435    .0126276
          avg_dur |   .0066416    .022883     0.29   0.772    -.0383189     .051602
          hh_kids |  -.1093802   .0397734    -2.75   0.006    -.1875268   -.0312336
mpreis_flats_rent |  -.0070896   .0259984    -0.27   0.785    -.0581712    .0439919
            _cons |   6.174431   10.54647     0.59   0.559    -14.54724    26.89611
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       493         493           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
. 
. ** PLOT 
.         // set legend 
.         local lorder `" order(1 "Local matching" 3 "Propensity score matching" 5 "Mahalanobis di
> stance matching"  7 "Entropy balancing weights" )"'

.                         
.         // PLOT: Turnouts Urne 
.         local d = "turnout_urne"

.         event_plot `d'_lm `d'_psm `d'_maha `d'_ebal, ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4(1)2) xtitl
> e("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `lorder' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel A.} Effect on Polling Place Turnout",nobox span bexpand justifi
> cation(left) size(medium)) ///
>                 name(urne, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry))  /
> //
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))

.         
.         // PLOT: Turnouts Postal
.         local d = "turnout_pos_req"

.         event_plot    `d'_lm `d'_psm `d'_maha `d'_ebal, ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4(1)2) xtitl
> e("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `lorder' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel B.} Effect on Mail-in Turnout",nobox span bexpand justification
> (left) size(medium)) ///
>                 name(postal, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry))   
> ///
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))

. 
.         // PLOT: Turnouts Total
.         local d = "turnout_tot_req"

.         event_plot  `d'_lm `d'_psm `d'_maha `d'_ebal, ///
>         stub_lag(L#event ) stub_lead(F#event ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4(1)2) xtitl
> e("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `lorder' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel C.} Effect on Total Turnout",nobox span bexpand justification(l
> eft) size(medium)) ///
>                 name(total, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry)) //
> /
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))   

.                 
.         * PLOT: FIGURE C3. Robustness to Matching on Observables–Pooled Reassignments
.         grc1leg2  urne postal total , xcommon  col(2) iscale(.7) name(bsl_rob_matching, replace)
>  ///
>         pos(4) ring(0) lcol(1) ltitle("Matching approach:")  ///
>                                 lxoffset(-8) lyoffset(15) legscale(*.9) ltsize(*.8)
-grc1leg2- working...

Note: To preserve in a saved graphics file the legend's row and column rearrangements
made with the options -lrows()- and/or -lcols()-, specify the suboption -asis-
for the saving() option or for the graph save command.


.         gr_edit .legend.title.DragBy 0 -8

.         graph export "$figures/Figure_C3_ES_matching_bsl.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C3_ES_
    > matching_bsl.pdf saved as PDF format

.         
.         
.                 
.         
. ********************************************************************************
.         // Matching: Heterogeneity by Increase/Decrease in distance (Figure C4) //
. ********************************************************************************                
.         
.         estimates clear 

.         
.         // maha distance matching       
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.          frame frmaha: reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b 
>  $ctr [aw=wahlber_gesamt] if smpl_trim==1 & smpl_maha==1 & Ei!=1, ///
>                 absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(mm_pair_id mm_ctr_id#i.wahl_id)
  3. 
. 
.                 estimates store `v'_a_maha
  4.                 estimates store `v'_b_maha
  5.         }               
(MWFE estimator converged in 8 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,065
Absorbing 2 HDFE groups                           F(  38,    269) =      10.44
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9745
                                                  Adj R-squared   =     0.9697
Number of clusters (mm_pair_id) =        270      Within R-sq.    =     0.2098
Number of clusters (mm_ctr_id#wahl_id) =      1,240Root MSE       =     1.5543

              (Std. err. adjusted for 270 clusters in mm_pair_id mm_ctr_id#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .4715365   .6019227     0.78   0.434     -.713542    1.656615
        F6event_a |   .1892332   .5754005     0.33   0.743     -.943628    1.322094
        F5event_a |   .2811455   .4128902     0.68   0.497    -.5317618    1.094053
        F4event_a |  -.0367102    .265157    -0.14   0.890    -.5587571    .4853367
        F3event_a |  -.1150779   .2605868    -0.44   0.659     -.628127    .3979711
        F2event_a |  -.0891268   .2038801    -0.44   0.662    -.4905304    .3122768
        L0event_a |   .5707382   .3720484     1.53   0.126    -.1617589    1.303235
        L1event_a |   .8104007   .3656745     2.22   0.028     .0904528    1.530349
        L2event_a |   .5347939    .397782     1.34   0.180    -.2483681    1.317956
        L3event_a |   .8143713   .3727822     2.18   0.030     .0804296    1.548313
        L4event_a |   .2172291   .8773148     0.25   0.805    -1.510048    1.944506
        L5event_a |   .3804568   1.527935     0.25   0.804    -2.627776    3.388689
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |  -.2290495   .4204914    -0.54   0.586    -1.056922    .5988232
        F6event_b |   .2092671   .3731201     0.56   0.575    -.5253399    .9438742
        F5event_b |   .3016309   .3187337     0.95   0.345    -.3258989    .9291608
        F4event_b |   .1834061   .2182641     0.84   0.401    -.2463171    .6131292
        F3event_b |   .1174256    .243388     0.48   0.630    -.3617621    .5966133
        F2event_b |   .1756274   .1627891     1.08   0.282    -.1448752    .4961301
        L0event_b |  -1.778903   .2918618    -6.10   0.000    -2.353527   -1.204279
        L1event_b |  -1.841016   .3076614    -5.98   0.000    -2.446747   -1.235286
        L2event_b |  -1.548636     .36381    -4.26   0.000    -2.264913   -.8323586
        L3event_b |  -.9445924   .4005537    -2.36   0.019    -1.733211   -.1559735
        L4event_b |  -1.781592   1.349367    -1.32   0.188    -4.438255    .8750714
        L5event_b |  -1.568467   .9570339    -1.64   0.102    -3.452697    .3157619
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -2.443622   1.314356    -1.86   0.064    -5.031356    .1441115
         ew_biodt |   .3107502   .0399703     7.77   0.000     .2320558    .3894446
        ew_dtmihi |    .007595   .0624647     0.12   0.903    -.1153869    .1305768
         ew_ledig |   .2447004   .0691545     3.54   0.000     .1085474    .3808533
       ew_married |   .4107689   .0678422     6.05   0.000     .2771997     .544338
        wb_anteil |   -.265527   .0316523    -8.39   0.000    -.3278447   -.2032093
          wb_ausl |   .0071073   .0197308     0.36   0.719    -.0317392    .0459538
         wb_18t24 |  -.0207714   .0370008    -0.56   0.575    -.0936193    .0520766
         wb_25t34 |   -.105909   .0277611    -3.82   0.000    -.1605657   -.0512522
         wb_35t44 |   .0008976   .0315422     0.03   0.977    -.0612034    .0629986
         wb_45t59 |  -.0133632    .026925    -0.50   0.620    -.0663737    .0396473
          avg_dur |  -.0253967   .0287251    -0.88   0.377    -.0819513     .031158
          hh_kids |  -.0276169   .0518595    -0.53   0.595    -.1297189    .0744852
mpreis_flats_rent |   .0143997   .0280138     0.51   0.608    -.0407545    .0695538
            _cons |   27.86829   11.77452     2.37   0.019     4.686367    51.05022
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           0         200     |
             sb_new |       425          25         400     |
------------------------------------------------------------+
(MWFE estimator converged in 8 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,065
Absorbing 2 HDFE groups                           F(  38,    269) =      12.08
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9658
                                                  Adj R-squared   =     0.9594
Number of clusters (mm_pair_id) =        270      Within R-sq.    =     0.2377
Number of clusters (mm_ctr_id#wahl_id) =      1,240Root MSE       =     1.5727

              (Std. err. adjusted for 270 clusters in mm_pair_id mm_ctr_id#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   -.715565   .5655248    -1.27   0.207    -1.828983    .3978526
        F6event_a |  -.2295623   .4918632    -0.47   0.641    -1.197953    .7388287
        F5event_a |  -.9614545   .4409698    -2.18   0.030    -1.829645   -.0932634
        F4event_a |  -.3111599    .233024    -1.34   0.183    -.7699426    .1476228
        F3event_a |   .0614326   .2313953     0.27   0.791    -.3941436    .5170088
        F2event_a |   -.022412   .2006015    -0.11   0.911    -.4173607    .3725366
        L0event_a |  -.5330534   .3312404    -1.61   0.109    -1.185207       .1191
        L1event_a |  -.4767219   .3374471    -1.41   0.159    -1.141095    .1876514
        L2event_a |   .0450366   .3739366     0.12   0.904     -.691178    .7812512
        L3event_a |  -.8037113   .3530512    -2.28   0.024    -1.498806   -.1086163
        L4event_a |   1.300041   .8617334     1.51   0.133    -.3965588     2.99664
        L5event_a |   1.671213    1.08842     1.54   0.126    -.4716914    3.814118
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |   .5685402   .3619381     1.57   0.117    -.1440515    1.281132
        F6event_b |   .3636824   .2929212     1.24   0.215    -.2130273    .9403921
        F5event_b |  -.0784788   .3420823    -0.23   0.819     -.751978    .5950203
        F4event_b |  -.1767445   .2067191    -0.85   0.393    -.5837375    .2302485
        F3event_b |  -.0483786   .1916981    -0.25   0.801    -.4257981    .3290409
        F2event_b |   .0117069   .1761241     0.07   0.947      -.33505    .3584639
        L0event_b |   1.169871   .2792469     4.19   0.000     .6200836    1.719659
        L1event_b |   1.711018   .2959443     5.78   0.000     1.128356     2.29368
        L2event_b |   1.701515   .3847038     4.42   0.000      .944102    2.458929
        L3event_b |   1.094639   .3647402     3.00   0.003     .3765301    1.812747
        L4event_b |    3.66135   1.020858     3.59   0.000     1.651462    5.671238
        L5event_b |   2.292347   1.086511     2.11   0.036     .1532013    4.431494
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   3.694338   1.430046     2.58   0.010     .8788326    6.509844
         ew_biodt |   .4255524    .036491    11.66   0.000     .3537081    .4973968
        ew_dtmihi |  -.2198121     .07274    -3.02   0.003    -.3630242   -.0765999
         ew_ledig |    .213511   .0944626     2.26   0.025      .027531    .3994911
       ew_married |   .2158115   .0927349     2.33   0.021     .0332331    .3983899
        wb_anteil |  -.2577402   .0317923    -8.11   0.000    -.3203335   -.1951469
          wb_ausl |  -.0833144   .0183601    -4.54   0.000    -.1194623   -.0471666
         wb_18t24 |   .0041094   .0368149     0.11   0.911    -.0683726    .0765915
         wb_25t34 |   .0877883   .0272718     3.22   0.001     .0340949    .1414816
         wb_35t44 |   .0124464   .0331509     0.38   0.708    -.0528219    .0777146
         wb_45t59 |  -.0032313    .026172    -0.12   0.902    -.0547593    .0482967
          avg_dur |   .0614532   .0307877     2.00   0.047     .0008376    .1220687
          hh_kids |  -.0992524   .0479766    -2.07   0.040    -.1937098   -.0047951
mpreis_flats_rent |   -.002688   .0294513    -0.09   0.927    -.0606725    .0552964
            _cons |  -24.81923   11.92144    -2.08   0.038    -48.29041   -1.348043
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           0         200     |
             sb_new |       425          25         400     |
------------------------------------------------------------+
(MWFE estimator converged in 8 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,065
Absorbing 2 HDFE groups                           F(  38,    269) =      22.47
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9915
                                                  Adj R-squared   =     0.9900
Number of clusters (mm_pair_id) =        270      Within R-sq.    =     0.4462
Number of clusters (mm_ctr_id#wahl_id) =      1,240Root MSE       =     1.4838

              (Std. err. adjusted for 270 clusters in mm_pair_id mm_ctr_id#wahl_id)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.2440298   .5905602    -0.41   0.680    -1.406738    .9186781
        F6event_a |  -.0403309   .4498229    -0.09   0.929    -.9259522    .8452903
        F5event_a |  -.6803089   .5070943    -1.34   0.181    -1.678687    .3180694
        F4event_a |    -.34787   .2699703    -1.29   0.199    -.8793935    .1836536
        F3event_a |  -.0536459   .2316815    -0.23   0.817    -.5097855    .4024937
        F2event_a |  -.1115385   .2343958    -0.48   0.635    -.5730221     .349945
        L0event_a |   .0376845   .2623549     0.14   0.886    -.4788456    .5542146
        L1event_a |   .3336784   .2970226     1.12   0.262    -.2511062    .9184631
        L2event_a |    .579831   .3223011     1.80   0.073    -.0547225    1.214384
        L3event_a |     .01066   .3256988     0.03   0.974    -.6305829     .651903
        L4event_a |   1.517271    .670581     2.26   0.024     .1970168    2.837526
        L5event_a |   2.051672   1.469594     1.40   0.164    -.8416966    4.945041
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |   .3394914   .4217244     0.81   0.422    -.4908088    1.169792
        F6event_b |   .5729487   .3640574     1.57   0.117    -.1438155    1.289713
        F5event_b |   .2231525    .344026     0.65   0.517    -.4541735    .9004786
        F4event_b |   .0066622   .2137002     0.03   0.975    -.4140754    .4273999
        F3event_b |   .0690468   .2192664     0.31   0.753    -.3626498    .5007434
        F2event_b |    .187335   .1824671     1.03   0.305    -.1719104    .5465803
        L0event_b |  -.6090311   .2122899    -2.87   0.004    -1.026992   -.1910701
        L1event_b |   -.129998   .2596863    -0.50   0.617    -.6412741     .381278
        L2event_b |   .1528797   .3088221     0.50   0.621    -.4551361    .7608955
        L3event_b |   .1500471   .3781873     0.40   0.692    -.5945363    .8946305
        L4event_b |   1.879757   .6054837     3.10   0.002     .6876678    3.071847
        L5event_b |    .723881    .555106     1.30   0.193    -.3690239    1.816786
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.250715   1.514799     0.83   0.410    -1.731655    4.233085
         ew_biodt |   .7363026   .0394704    18.65   0.000     .6585924    .8140128
        ew_dtmihi |   -.212217   .0681381    -3.11   0.002    -.3463688   -.0780653
         ew_ledig |   .4582115   .0853115     5.37   0.000     .2902483    .6261747
       ew_married |   .6265805   .0823183     7.61   0.000     .4645104    .7886506
        wb_anteil |  -.5232672   .0334691   -15.63   0.000    -.5891619   -.4573724
          wb_ausl |  -.0762071    .018124    -4.20   0.000    -.1118901   -.0405241
         wb_18t24 |   -.016662   .0319423    -0.52   0.602    -.0795507    .0462267
         wb_25t34 |  -.0181207   .0213156    -0.85   0.396    -.0600873    .0238459
         wb_35t44 |    .013344    .027054     0.49   0.622    -.0399205    .0666085
         wb_45t59 |  -.0165945   .0248564    -0.67   0.505    -.0655323    .0323433
          avg_dur |   .0360565   .0304734     1.18   0.238    -.0239402    .0960531
          hh_kids |  -.1268693   .0442581    -2.87   0.004    -.2140056   -.0397331
mpreis_flats_rent |   .0117117   .0277304     0.42   0.673    -.0428845    .0663079
            _cons |   3.049064   12.99415     0.23   0.815     -22.5341    28.63223
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           0         200     |
             sb_new |       425          25         400     |
------------------------------------------------------------+

.         
.         // propensity score matching    
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b  $ctr [
> aw=wahlber_gesamt] if smpl_trim==1 & smpl_psm==1 & Ei!=1, ///
>                         absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
  3. 
. 
.                 estimates store `v'_a_psm
  4.                 estimates store `v'_b_psm
  5.         }               
(MWFE estimator converged in 8 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,036
Absorbing 2 HDFE groups                           F(  38,    535) =      12.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9727
                                                  Adj R-squared   =     0.9662
                                                  Within R-sq.    =     0.2071
Number of clusters (sb_new)  =        536         Root MSE        =     1.6506

                                    (Std. err. adjusted for 536 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |    .525335   .5749941     0.91   0.361    -.6041879    1.654858
        F6event_a |   .2202119   .5518209     0.40   0.690    -.8637894    1.304213
        F5event_a |    .281953    .387652     0.73   0.467    -.4795536     1.04346
        F4event_a |  -.0773728   .2457753    -0.31   0.753    -.5601756    .4054301
        F3event_a |  -.1706504   .2408809    -0.71   0.479    -.6438387    .3025379
        F2event_a |   -.156286   .1804887    -0.87   0.387    -.5108395    .1982675
        L0event_a |   .3734758   .3425724     1.09   0.276    -.2994761    1.046428
        L1event_a |   .5820101   .3128933     1.86   0.063      -.03264     1.19666
        L2event_a |   .4496891   .3604983     1.25   0.213    -.2584766    1.157855
        L3event_a |   .6154748   .3664979     1.68   0.094    -.1044767    1.335426
        L4event_a |   .1839231   .7064151     0.26   0.795    -1.203764    1.571611
        L5event_a |   .5215614   1.444926     0.36   0.718    -2.316863    3.359986
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |  -.1894737   .4202819    -0.45   0.652    -1.015079    .6361314
        F6event_b |   .3281403   .3627826     0.90   0.366    -.3845127    1.040793
        F5event_b |   .3220621   .3071082     1.05   0.295    -.2812236    .9253479
        F4event_b |   .2085965   .2146179     0.97   0.332    -.2130006    .6301937
        F3event_b |   .1108602   .2092714     0.53   0.597    -.3002342    .5219547
        F2event_b |   .1687089    .148143     1.14   0.255    -.1223044    .4597223
        L0event_b |  -1.885519   .2696591    -6.99   0.000     -2.41524   -1.355799
        L1event_b |  -1.950094   .2753958    -7.08   0.000    -2.491084   -1.409105
        L2event_b |  -1.604522   .3168218    -5.06   0.000    -2.226889   -.9821545
        L3event_b |  -1.055429   .3388077    -3.12   0.002    -1.720985   -.3898723
        L4event_b |  -1.354679   1.062778    -1.27   0.203    -3.442409      .73305
        L5event_b |  -1.385792   1.030281    -1.35   0.179    -3.409684    .6381003
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -.7720951   .9979557    -0.77   0.439    -2.732487    1.188297
         ew_biodt |   .3443699   .0316633    10.88   0.000     .2821703    .4065695
        ew_dtmihi |   .0288545   .0541909     0.53   0.595    -.0775986    .1353075
         ew_ledig |   .1930133   .0575744     3.35   0.001     .0799136     .306113
       ew_married |   .4098581   .0602426     6.80   0.000     .2915171     .528199
        wb_anteil |  -.2630122   .0232255   -11.32   0.000    -.3086366   -.2173877
          wb_ausl |   .0016802   .0148393     0.11   0.910    -.0274703    .0308307
         wb_18t24 |  -.0336033   .0313895    -1.07   0.285     -.095265    .0280584
         wb_25t34 |  -.0591953    .019577    -3.02   0.003    -.0976525   -.0207382
         wb_35t44 |   .0005307   .0241103     0.02   0.982    -.0468318    .0478931
         wb_45t59 |   .0044564   .0229348     0.19   0.846    -.0405969    .0495097
          avg_dur |  -.0238295   .0227459    -1.05   0.295    -.0685117    .0208527
          hh_kids |  -.0366245   .0430241    -0.85   0.395    -.1211414    .0478925
mpreis_flats_rent |   .0271704   .0245865     1.11   0.270    -.0211274    .0754683
            _cons |   13.58584   9.214041     1.47   0.141    -4.514295    31.68598
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       536         536           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,036
Absorbing 2 HDFE groups                           F(  38,    535) =      13.23
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9637
                                                  Adj R-squared   =     0.9552
                                                  Within R-sq.    =     0.2262
Number of clusters (sb_new)  =        536         Root MSE        =     1.6425

                                    (Std. err. adjusted for 536 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.9783864   .5355076    -1.83   0.068    -2.030342    .0735691
        F6event_a |  -.3695507   .4681741    -0.79   0.430    -1.289236    .5501341
        F5event_a |  -1.102711   .4131768    -2.67   0.008    -1.914359    -.291063
        F4event_a |  -.3772984   .2239182    -1.68   0.093    -.8171652    .0625684
        F3event_a |    .047793    .225859     0.21   0.832    -.3958863    .4914723
        F2event_a |  -.0358118   .1857611    -0.19   0.847    -.4007223    .3290987
        L0event_a |  -.3767312   .3061784    -1.23   0.219    -.9781906    .2247281
        L1event_a |  -.3303556   .3080436    -1.07   0.284    -.9354789    .2747678
        L2event_a |   .0634016    .373771     0.17   0.865    -.6708371    .7976404
        L3event_a |  -.4853383    .341627    -1.42   0.156    -1.156433    .1857566
        L4event_a |   1.082307   .7886895     1.37   0.171    -.4670007    2.631615
        L5event_a |   2.256567   1.037476     2.18   0.030     .2185398    4.294593
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |   .3595352   .3387028     1.06   0.289    -.3058153    1.024886
        F6event_b |   .2119927   .2704396     0.78   0.433    -.3192609    .7432463
        F5event_b |  -.3012716   .3117674    -0.97   0.334      -.91371    .3111669
        F4event_b |  -.2206468   .1980613    -1.11   0.266      -.60972    .1684264
        F3event_b |  -.1255295   .1845199    -0.68   0.497    -.4880019     .236943
        F2event_b |  -.1013436   .1477881    -0.69   0.493    -.3916597    .1889724
        L0event_b |   1.262728   .2601875     4.85   0.000     .7516141    1.773843
        L1event_b |   1.850846   .2686275     6.89   0.000     1.323152     2.37854
        L2event_b |   1.839756   .3322726     5.54   0.000     1.187037    2.492475
        L3event_b |   1.436899    .335856     4.28   0.000     .7771412    2.096658
        L4event_b |   3.354049   .5923132     5.66   0.000     2.190504    4.517594
        L5event_b |   2.748935   .7550508     3.64   0.000     1.265707    4.232163
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.193616   1.394954     1.57   0.116    -.5466426    4.933874
         ew_biodt |   .4025132   .0321157    12.53   0.000     .3394248    .4656016
        ew_dtmihi |  -.2103827   .0598352    -3.52   0.000    -.3279234    -.092842
         ew_ledig |   .2356267   .0839079     2.81   0.005     .0707973    .4004561
       ew_married |   .2655989   .0827905     3.21   0.001     .1029647    .4282332
        wb_anteil |  -.2645077   .0249451   -10.60   0.000    -.3135099   -.2155054
          wb_ausl |  -.0709302   .0143534    -4.94   0.000    -.0991262   -.0427343
         wb_18t24 |  -.0147479   .0298928    -0.49   0.622    -.0734696    .0439737
         wb_25t34 |   .0416231   .0197123     2.11   0.035        .0029    .0803461
         wb_35t44 |   .0072399    .025726     0.28   0.778    -.0432966    .0577763
         wb_45t59 |  -.0189835   .0219638    -0.86   0.388    -.0621294    .0241624
          avg_dur |   .0476938   .0253383     1.88   0.060     -.002081    .0974686
          hh_kids |  -.1038413   .0427399    -2.43   0.015    -.1877999   -.0198827
mpreis_flats_rent |  -.0147756   .0246966    -0.60   0.550    -.0632898    .0337386
            _cons |  -12.34405   11.75154    -1.05   0.294    -35.42886    10.74076
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       536         536           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,036
Absorbing 2 HDFE groups                           F(  38,    535) =      32.27
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9908
                                                  Adj R-squared   =     0.9886
                                                  Within R-sq.    =     0.4431
Number of clusters (sb_new)  =        536         Root MSE        =     1.5772

                                    (Std. err. adjusted for 536 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.4530529   .5077605    -0.89   0.373    -1.450502     .544396
        F6event_a |  -.1493402   .3927651    -0.38   0.704    -.9208912    .6222108
        F5event_a |  -.8207575   .4572908    -1.79   0.073    -1.719063    .0775483
        F4event_a |  -.4546713   .2497143    -1.82   0.069    -.9452121    .0358695
        F3event_a |   -.122858   .2191779    -0.56   0.575    -.5534128    .3076968
        F2event_a |  -.1920977   .2062075    -0.93   0.352    -.5971733    .2129779
        L0event_a |  -.0032557   .2425107    -0.01   0.989    -.4796458    .4731343
        L1event_a |   .2516543   .2867189     0.88   0.380    -.3115785    .8148872
        L2event_a |   .5130913   .3185521     1.61   0.108    -.1126751    1.138858
        L3event_a |   .1301364   .3022708     0.43   0.667    -.4636468    .7239196
        L4event_a |   1.266231   .5927292     2.14   0.033     .1018694    2.430593
        L5event_a |   2.778129    1.71956     1.62   0.107    -.5997883    6.156047
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |   .1700621   .3837561     0.44   0.658    -.5837913    .9239156
        F6event_b |   .5401327   .3428749     1.58   0.116    -.1334136    1.213679
        F5event_b |    .020791   .2945744     0.07   0.944    -.5578734    .5994553
        F4event_b |  -.0120496   .1940268    -0.06   0.951    -.3931974    .3690982
        F3event_b |  -.0146692   .1994322    -0.07   0.941    -.4064354    .3770971
        F2event_b |   .0673659   .1641314     0.41   0.682    -.2550552    .3897869
        L0event_b |  -.6227897   .2024375    -3.08   0.002    -1.020459   -.2251199
        L1event_b |  -.0992477   .2570533    -0.39   0.700    -.6042053    .4057098
        L2event_b |    .235234     .28582     0.82   0.411     -.326233    .7967011
        L3event_b |   .3814714    .319473     1.19   0.233    -.2461039    1.009047
        L4event_b |   1.999369    .750111     2.67   0.008     .5258448    3.472893
        L5event_b |   1.363144   .5879911     2.32   0.021     .2080899    2.518199
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.421522   1.123985     1.26   0.207    -.7864442    3.629488
         ew_biodt |   .7468831   .0344657    21.67   0.000     .6791784    .8145877
        ew_dtmihi |  -.1815281   .0560011    -3.24   0.001    -.2915372    -.071519
         ew_ledig |   .4286402   .0760692     5.63   0.000     .2792092    .5780712
       ew_married |   .6754571   .0735672     9.18   0.000     .5309412     .819973
        wb_anteil |  -.5275198   .0269078   -19.60   0.000    -.5803778   -.4746619
          wb_ausl |  -.0692501   .0157396    -4.40   0.000    -.1001691    -.038331
         wb_18t24 |  -.0483513   .0271676    -1.78   0.076    -.1017196     .005017
         wb_25t34 |  -.0175723   .0170072    -1.03   0.302    -.0509814    .0158368
         wb_35t44 |   .0077705    .021669     0.36   0.720    -.0347962    .0503373
         wb_45t59 |  -.0145271   .0208096    -0.70   0.485    -.0554057    .0263515
          avg_dur |   .0238643     .02584     0.92   0.356     -.026896    .0746247
          hh_kids |  -.1404658   .0369948    -3.80   0.000    -.2131388   -.0677929
mpreis_flats_rent |   .0123949   .0245425     0.51   0.614    -.0358167    .0606064
            _cons |   1.241773   10.42649     0.12   0.905    -19.24011    21.72366
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       536         536           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                 
.         // Local Matching 
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                 qui reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b $ct
> r [aw=wahlber_gesamt] ///
>                                         if  smpl_trim==1 & smpl_lmatch==1 , absorb(i.wahl_id#i.s
> tadtbez i.sb_new) cluster(sb_new)
  3. 
. 
.                 estimates store `v'_a_lm
  4.                 estimates store `v'_b_lm        
  5.         }               

.         
.         // Entropy balancing 
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                 qui reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b $ct
> r [aw=_webal]  ///
>                                         if  smpl_trim==1 & Ei!=1 , absorb(i.wahl_id#i.stadtbez i
> .sb_new) cluster(sb_new)
  3. 
. 
.                 estimates store `v'_a_ebal
  4.                 estimates store `v'_b_ebal      
  5.         }               

.         
. ** PLOT Results 
.         
.  foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req { 
  2.                 
.         // set legend 
.         local lorder `" order(1 "Local matching" 3 "Propensity score matching" 5 "Mahalanobis di
> stance matching"  7 "Entropy balancing weights" )"'
  3.         
.                 // PLOT Dist Decrease
.                 event_plot  `v'_a_lm `v'_a_psm `v'_a_maha  `v'_a_ebal , ///
>                 stub_lag(L#event_a ) stub_lead(F#event_a) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4(1)2) xtitl
> e("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `lorder' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 subtitle("{bf:b.} Distance decrease",nobox justification(left) size(medsmall)) /
> //
>                 name(`v'_dwn, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry)) //
> /
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))
  4.         
.                 // PLOT Dist Increase
.                 event_plot   `v'_b_lm `v'_b_psm `v'_b_maha  `v'_b_ebal , ///
>                 stub_lag(L#event_b ) stub_lead(F#event_b) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4(1)2) xtitl
> e("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `lorder' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 subtitle("{bf:a.} Distance increase",nobox justification(left) size(medsmall)) /
> //
>                 name(`v'_up, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry)) //
> /
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))
  5.         
.         
.  }      

. 
.                 
.         * PLOT: FIGURE C4. Robustness to Matching on Observables–Effects by Distance Change
.         grc1leg2 turnout_urne_up turnout_urne_dwn,               name(g1, replace) ///
>                         title("{bf:Panel A.} Effect on Polling Place Turnout", just(left) bexpan
> d size(small)) loff  iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 turnout_pos_req_up turnout_pos_req_dwn, name(g2, replace) ///
>                         title("{bf:Panel B.} Effect on Mail-in Turnout", just(left) bexpand size
> (small)) loff  iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 turnout_tot_req_up turnout_tot_req_dwn, name(g3, replace) ///
>                         title("{bf:Panel C.} Effect on Total Turnout", just(left) bexpand size(s
> mall))   iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 g1 g2 g3, col(1) imargins(zero) legscale(*.65) lrow(2)
-grc1leg2- working...

Note: To preserve in a saved graphics file the legend's row and column rearrangements
made with the options -lrows()- and/or -lcols()-, specify the suboption -asis-
for the saving() option or for the graph save command.


.         gr_edit .style.editstyle declared_ysize(6) editcopy     

.         
.         graph export "$figures/Figure_C4_ES_matching_dist2.pdf", replace 
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C4_ES_
    > matching_dist2.pdf saved as PDF format

.         
.         
. ********************************************************************************
. // Visualization of local matching outcome: MAP of matched precincts (Figure C5) //
. ********************************************************************************        
. 
. ** Plot: Local Matching, treated vs. matched control units      
. use "$newdata/estimation_prep_ltw18.dta", clear 

. 
.         // merge with local matched sample 
.         merge 1:1 wahl_id sb_new using "$tmp/local_matching_sample_1_within_district.dta", asser
> t(1 3) nogen 
(variable wahl_id was byte, now float to accommodate using data's values)

    Result                      Number of obs
    -----------------------------------------
    Not matched                           936
        from master                       936  
        from using                          0  

    Matched                             4,008  
    -----------------------------------------

. 
.         // keep any election 
.         keep if wahl_id==1
(4,326 observations deleted)

.         
.         merge 1:n sb_new using "$newdata/ltw18_sb_mapping.dta", assert(3) nogen

    Result                      Number of obs
    -----------------------------------------
    Not matched                             0
    Matched                               620  
    -----------------------------------------

.         
.         // gen help variable 1=matched treated, 2 = matched control 3 = not matched 
.         gen     help = 1 if T==1 &smpl_lmatch==1 
(372 missing values generated)

.         replace help = 2 if T==0 &smpl_lmatch==1 
(254 real changes made)

.         replace help = 3 if smpl_lmatch==.
(118 real changes made)

.         
.         
.         * PLOT: FIGURE C5. Map of Matched Treated and Control Precincts
.         spmap help using "$shp_path/landtagwahl2018_stimmbezirke_newprj_shp.dta", ///
>         id(_ID)  /*clmethod(quantile) cln(10)*/ clmethod(unique) fcol(navy%40 cranberry%60 white
> ) ///
>         osize(vthin ...) ocol(black%20 ...) /* ndsize(vthin) ndocol(black%40) ndfcolor(none)*/ /
> //
>         polygon(data("$shp_path/stadtbezirke_shp.dta") ocol(black ..) osize(*1 ..) fcol(none ..)
>  opat(solid ..)) ysize(3)  ///
>         legend( pos(8) order(2 "Matched {bf:treated} precincts" 3 "Matched {bf:control} (never-t
> reated) precincts"  4 "Unmatched units" )  )

.         gr_edit .legend.DragBy 10 -9

.         graph export "$figures/Figure_C5_map_local_matching_smpl.pdf", replace  
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C5_map
    > _local_matching_smpl.pdf saved as PDF format

.         
.                 
. 
end of do-file
Running: 04h_rob_covariates_tables_c5_c6.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output: Table C.5, C.6 
> 
> Task: Sensitivity to inclusion of covariates
> 
> 
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
. ********************************************************************************
.                 // Prep Estimation //
. ********************************************************************************
.         
.                                         
.         // controls to be interacted with time dummies  
.         global intctr   ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_married area_sb_ltw18 ///
>                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 avg_dur 
> hh_kids mpreis_flats_rent       ///
>                                  street_dist

.                                  
.                                 
.         // gen 2013 constant variables: suffix _2013
.         foreach v of varlist $intctr {
  2.                 gen `v'_2013 = `v' if wahl_id==1
  3.                 grconst `v'_2013, by(sb_new) fill
  4.         }
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)
(4,326 missing values generated)
(4326 real changes made)

.         
.         // compute group ids for DISTANCE increase/decrease, 0 else
.         gen     tmp = (del_street_dist>0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase in event, 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease in event, 0 else"              

.         
.         // Relabel outcomes for tables 
.         lab var turnout_urne    "Polling place turnout"

.         lab var turnout_pos_req "Mail-in turnout"

.         lab var turnout_tot_req "Total turnout"

.         
.         
. * TWFE OLS: gen leads and lags
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen     F`l'event = K==-`l'
  3.                 lab var F`l'event "Reassignment (#t-`l'#)"
  4.         }       

.         forvalues l = 0/7 {
  2.                 gen     L`l'event = K==`l'
  3.                 lab var L`l'event "Reassignment (#t+`l'#)"
  4.         }

.         
.         
. * TWFE OLS: gen leads and lags by distance
.         // Create two set of dummies: dist dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_dn                            // a 
> := decrease
  3.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b = F`l'event *ind_dist_up                         
>    // b:= increase
  5.                 lab var F`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6. 
.                 assert  F`l'event_b+F`l'event_a==F`l'event              
  7.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_dn    // a := decrease
  3.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b = L`l'event *ind_dist_up    // b:= increase
  5.                 lab var L`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 assert  L`l'event_b+L`l'event_a==L`l'event
  7.         }       

.                 
. 
.         
.         
. ********************************************************************************
.                 // Baseline Specification: Robustness to inclusion of covariates //
. ********************************************************************************        
. 
. 
.         order F1event, last

.                 
.         global order  F4event F3event F2event /*F1event*/ L0event L1event L2event               

. 
.         estimates clear

.         outreg, clear 

.         
.         local j=0

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                 
.                 //  NO CONTROLS
.                 local `++j'
  3.                  reghdfe `v' F7event-L7event F1event /*$ctr*/ $wgt if smpl_trim==1, absorb(i.
> wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
  4.                 
.                 estimates store `v'_noctr
  5.                 qui outreg,  $opt  keep($order)  ctitle("", "(`j')") addrow(Time-varying cont
> rols, none )       
  6.         
.                 
.         // SOME TIME-VARYING CONTROLS (not in graph output only table)                          
.                 local `++j'
  7.                 local somectr_1= "ln_ew_ges wb_anteil ew_biodt ew_dtmihi " 
  8.                  reghdfe `v' F7event-L7event F1event `somectr_1' $wgt if smpl_trim==1, absorb
> (i.wahl_id#i.stadtbez i.sb_new ) cluster(sb_new)
  9.                 
.                 estimates store `v'_some1
 10.                 qui outreg,  $opt  keep($order)  ctitle("", "(`j')") addrow(Time-varying cont
> rols, some )
 11.                         
.                 
.         // ALL TIME-VARYING CONTROLS (Baseline)
.                 local `++j'
 12.                  reghdfe `v' F7event-L7event F1event $ctr $wgt if smpl_trim==1, absorb(i.wahl
> _id#i.stadtbez i.sb_new ) cluster(sb_new)
 13.                 
.                 estimates store `v'_bsl
 14.                 qui outreg,  $opt  keep($order)  ctitle("", "(`j')" \"", "`:var lab `v''") ad
> drow(Time-varying controls, all )
 15.         
.         
.         // PRE-TREATMENT CONTROLS (2013) x election FE; excluding units treated in 2013 (no time
> -varying controls)
.                  local `++j'
 16.                   reghdfe `v' F7event-L7event F1event /*$ctr*/  if smpl_trim==1 & Ei!=1, abso
> rb(i.wahl_id#i.stadtbez i.sb_new c.(*_2013)##i.wahl_id) cluster(sb_new)
 17.                 
.                 estimates store `v'_int
 18.                 qui outreg,  $opt  keep($order)  ctitle("", "(`j')") addrow(Time-varying cont
> rols, none \ Pre-treatment covariates x election FE, yes )
 19.         }       
(MWFE estimator converged in 7 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  14,    617) =       2.81
Statistics robust to heteroskedasticity           Prob > F        =     0.0004
                                                  R-squared       =     0.9669
                                                  Adj R-squared   =     0.9597
                                                  Within R-sq.    =     0.0182
Number of clusters (sb_new)  =        618         Root MSE        =     1.8423

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |  -.2674328   .4045889    -0.66   0.509    -1.061971    .5271054
     F6event |  -.0815952   .3488006    -0.23   0.815    -.7665755    .6033852
     F5event |   .0318923   .2913204     0.11   0.913    -.5402074    .6039921
     F4event |  -.0510246   .1860281    -0.27   0.784    -.4163497    .3143004
     F3event |  -.0795773   .1813095    -0.44   0.661    -.4356359    .2764812
     F2event |  -.1316265   .1432767    -0.92   0.359    -.4129956    .1497426
     L0event |  -1.115832    .243541    -4.58   0.000    -1.594102   -.6375621
     L1event |  -.9750457   .2496525    -3.91   0.000    -1.465317    -.484774
     L2event |  -.7479335   .2780469    -2.69   0.007    -1.293967   -.2019005
     L3event |   -.454083   .2645428    -1.72   0.087    -.9735965    .0654305
     L4event |  -1.012262   .5151232    -1.97   0.050    -2.023869   -.0006546
     L5event |   -.705731   .5721307    -1.23   0.218     -1.82929    .4178286
     L6event |   .8914871   .9166137     0.97   0.331    -.9085738    2.691548
     L7event |  -.0116847   1.344213    -0.01   0.993    -2.651471    2.628102
     F1event |          0  (omitted)
       _cons |   34.10401   .0556651   612.66   0.000     33.99469    34.21332
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  18,    617) =      20.42
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9707
                                                  Adj R-squared   =     0.9644
                                                  Within R-sq.    =     0.1320
Number of clusters (sb_new)  =        618         Root MSE        =     1.7331

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |  -.2549861   .3780706    -0.67   0.500    -.9974474    .4874751
     F6event |  -.0450755   .3335515    -0.14   0.893    -.7001094    .6099584
     F5event |   .1947534   .2620527     0.74   0.458      -.31987    .7093768
     F4event |  -.0165486   .1789522    -0.09   0.926    -.3679778    .3348806
     F3event |  -.0760887   .1745922    -0.44   0.663    -.4189557    .2667783
     F2event |   .0274407   .1230677     0.22   0.824    -.2142416     .269123
     L0event |  -1.057454   .2371436    -4.46   0.000    -1.523161   -.5917478
     L1event |  -.9742916   .2403484    -4.05   0.000    -1.446292   -.5022915
     L2event |  -.7723221    .265977    -2.90   0.004    -1.294652   -.2499921
     L3event |  -.3050276   .2758222    -1.11   0.269    -.8466917    .2366365
     L4event |    -.91598    .490151    -1.87   0.062    -1.878547    .0465866
     L5event |  -.7423868   .6262652    -1.19   0.236    -1.972257     .487483
     L6event |   .7754293   .8340488     0.93   0.353    -.8624893    2.413348
     L7event |   .5005265   1.355746     0.37   0.712     -2.16191    3.162963
     F1event |          0  (omitted)
   ln_ew_ges |  -.5899126   .8129011    -0.73   0.468    -2.186301    1.006476
   wb_anteil |  -.2920513   .0206251   -14.16   0.000    -.3325552   -.2515474
    ew_biodt |   .4090753   .0255773    15.99   0.000     .3588462    .4593043
   ew_dtmihi |   .1001765   .0507398     1.97   0.049     .0005329    .1998201
       _cons |   31.92077   7.122893     4.48   0.000     17.93271    45.90882
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      17.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9658
                                                  Within R-sq.    =     0.1691
Number of clusters (sb_new)  =        618         Root MSE        =     1.6979

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3609916    -0.46   0.646    -.8749677    .5428746
          F6event |   .0365542   .3229698     0.11   0.910    -.5976991    .6708075
          F5event |   .2427259   .2553086     0.95   0.342    -.2586533    .7441051
          F4event |   .0132213   .1747424     0.08   0.940    -.3299406    .3563833
          F3event |  -.0565081    .169943    -0.33   0.740    -.3902449    .2772287
          F2event |    .006289   .1214372     0.05   0.959    -.2321913    .2447694
          L0event |  -.9985596   .2347416    -4.25   0.000    -1.459549   -.5375702
          L1event |   -.892731   .2344548    -3.81   0.000    -1.353157   -.4323048
          L2event |  -.7515249   .2578216    -2.91   0.004    -1.257839   -.2452106
          L3event |  -.2931705   .2663112    -1.10   0.271    -.8161567    .2298157
          L4event |  -.8782313   .4731179    -1.86   0.064    -1.807348    .0508853
          L5event |  -.5487131   .6172865    -0.89   0.374     -1.76095    .6635241
          L6event |    1.07717   .7208125     1.49   0.136    -.3383731    2.492714
          L7event |   .9427971   1.187553     0.79   0.428    -1.389339    3.274933
          F1event |          0  (omitted)
        ln_ew_ges |  -.7878053   .9548651    -0.83   0.410    -2.662985    1.087374
         ew_biodt |    .373603   .0281093    13.29   0.000     .3184015    .4288045
        ew_dtmihi |   .0630129   .0513196     1.23   0.220    -.0377693    .1637951
         ew_ledig |   .2127399   .0574696     3.70   0.000     .0998801    .3255996
       ew_married |   .4312672   .0590852     7.30   0.000     .3152348    .5472997
        wb_anteil |  -.2895078   .0204524   -14.16   0.000    -.3296726   -.2493431
          wb_ausl |   .0153767   .0162706     0.95   0.345    -.0165758    .0473293
         wb_18t24 |  -.0164981   .0295896    -0.56   0.577    -.0746067    .0416105
         wb_25t34 |  -.0724461   .0193717    -3.74   0.000    -.1104886   -.0344037
         wb_35t44 |    .006101   .0230068     0.27   0.791    -.0390802    .0512822
         wb_45t59 |   .0140084   .0220328     0.64   0.525      -.02926    .0572768
          avg_dur |  -.0288813   .0210954    -1.37   0.171    -.0703088    .0125462
          hh_kids |  -.0506236   .0408723    -1.24   0.216    -.1308893    .0296421
mpreis_flats_rent |   .0303932   .0255105     1.19   0.234    -.0197047    .0804911
            _cons |   11.36038   9.043266     1.26   0.210    -6.398933    29.11969
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 16 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 3 HDFE groups                           F(  12,    607) =       3.73
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9649
                                                  Within R-sq.    =     0.0265
Number of clusters (sb_new)  =        608         Root MSE        =     1.6919

                               (Std. err. adjusted for 608 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |    .171689   .3957397     0.43   0.665    -.6054962    .9488743
     F6event |   .2494792   .3412073     0.73   0.465     -.420611    .9195694
     F5event |   .3398516   .2932616     1.16   0.247     -.236079    .9157821
     F4event |   .1834503   .1791123     1.02   0.306    -.1683047    .5352052
     F3event |   .0148649    .167392     0.09   0.929    -.3138728    .3436026
     F2event |   .0638823   .1266958     0.50   0.614    -.1849331    .3126977
     L0event |  -1.176113   .2293339    -5.13   0.000    -1.626497   -.7257285
     L1event |  -.9083226   .2512857    -3.61   0.000    -1.401818   -.4148277
     L2event |  -.7205919   .2874633    -2.51   0.012    -1.285135   -.1560485
     L3event |  -.5423246   .2686408    -2.02   0.044    -1.069903   -.0147463
     L4event |  -1.455978   .6331176    -2.30   0.022    -2.699345   -.2126115
     L5event |  -.7861751   .6976987    -1.13   0.260    -2.156372    .5840214
     L6event |          0  (omitted)
     L7event |          0  (omitted)
     F1event |          0  (omitted)
       _cons |   34.59132   .0537006   644.15   0.000     34.48586    34.69678
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------------------------+
                      Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------------------------+---------------------------------------|
                 wahl_id#stadtbez |       200           1         199     |
                           sb_new |       608         608           0    *|
                          wahl_id |         8           8           0     |
         wahl_id#c.ln_ew_ges_2013 |         8           0           8    ?|
          wahl_id#c.ew_biodt_2013 |         8           0           8    ?|
         wahl_id#c.ew_dtmihi_2013 |         8           0           8    ?|
          wahl_id#c.ew_ledig_2013 |         8           0           8    ?|
        wahl_id#c.ew_married_2013 |         8           0           8    ?|
     wahl_id#c.area_sb_ltw18_2013 |         8           0           8    ?|
         wahl_id#c.wb_anteil_2013 |         8           0           8    ?|
           wahl_id#c.wb_ausl_2013 |         8           8           0    ?|
          wahl_id#c.wb_18t24_2013 |         8           0           8    ?|
          wahl_id#c.wb_25t34_2013 |         8           0           8    ?|
          wahl_id#c.wb_35t44_2013 |         8           0           8    ?|
          wahl_id#c.wb_45t59_2013 |         8           0           8    ?|
           wahl_id#c.avg_dur_2013 |         8           0           8    ?|
           wahl_id#c.hh_kids_2013 |         8           0           8    ?|
 wahl_id#c.mpreis_flats_rent_2013 |         8           0           8    ?|
       wahl_id#c.street_dist_2013 |         8           0           8    ?|
--------------------------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 7 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  14,    617) =       5.18
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9526
                                                  Adj R-squared   =     0.9424
                                                  Within R-sq.    =     0.0183
Number of clusters (sb_new)  =        618         Root MSE        =     1.8676

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |   .0714541   .3303636     0.22   0.829    -.5773192    .7202275
     F6event |   .1849191   .2862281     0.65   0.518    -.3771803    .7470184
     F5event |  -.6332938    .279148    -2.27   0.024    -1.181489   -.0850984
     F4event |  -.2025801   .1762875    -1.15   0.251    -.5487763    .1436161
     F3event |   .0957791   .1639782     0.58   0.559     -.226244    .4178022
     F2event |  -.1849395   .1292271    -1.43   0.153    -.4387178    .0688388
     L0event |   .5466877   .2278584     2.40   0.017     .0992157    .9941598
     L1event |   .8792793   .2420927     3.63   0.000     .4038537    1.354705
     L2event |   .9035226   .2924361     3.09   0.002     .3292318    1.477813
     L3event |   .1955319   .3207377     0.61   0.542    -.4343379    .8254017
     L4event |   1.425625   .6509723     2.19   0.029      .147235    2.704015
     L5event |   2.203663   .6605711     3.34   0.001     .9064232    3.500904
     L6event |  -.5782607   .8088805    -0.71   0.475    -2.166753    1.010232
     L7event |  -1.356748   .6633143    -2.05   0.041    -2.659376   -.0541208
     F1event |          0  (omitted)
       _cons |   28.43522    .050006   568.64   0.000     28.33702    28.53342
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  18,    617) =      20.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9606
                                                  Adj R-squared   =     0.9520
                                                  Within R-sq.    =     0.1838
Number of clusters (sb_new)  =        618         Root MSE        =     1.7038

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |   .1348501   .3353883     0.40   0.688    -.5237909    .7934912
     F6event |   .2814106   .2707058     1.04   0.299    -.2502057     .813027
     F5event |  -.4410994   .2654389    -1.66   0.097    -.9623726    .0801738
     F4event |  -.1661824   .1602466    -1.04   0.300    -.4808773    .1485125
     F3event |   .1022779   .1557617     0.66   0.512    -.2036095    .4081653
     F2event |    -.03163    .126614    -0.25   0.803    -.2802766    .2170167
     L0event |   .6212436   .2176777     2.85   0.004     .1937646    1.048723
     L1event |    .925467   .2279979     4.06   0.000     .4777211    1.373213
     L2event |   .9934709   .2693317     3.69   0.000     .4645529    1.522389
     L3event |   .4348988   .2670441     1.63   0.104    -.0895266    .9593243
     L4event |   1.443867   .6529151     2.21   0.027     .1616619    2.726072
     L5event |   2.245118   .5538803     4.05   0.000     1.157399    3.332837
     L6event |  -.5994345   .9350812    -0.64   0.522    -2.435762    1.236893
     L7event |  -.5861234   .7517224    -0.78   0.436    -2.062368    .8901212
     F1event |          0  (omitted)
   ln_ew_ges |   2.324129    1.32856     1.75   0.081    -.2849198    4.933177
   wb_anteil |  -.2831296   .0239921   -11.80   0.000    -.3302458   -.2360135
    ew_biodt |   .4336954   .0285052    15.21   0.000     .3777163    .4896744
   ew_dtmihi |  -.2511739   .0526205    -4.77   0.000    -.3545108   -.1478369
       _cons |   6.635251   11.13769     0.60   0.552    -15.23713    28.50763
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9616
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2040
Number of clusters (sb_new)  =        618         Root MSE        =     1.6847

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3314703     0.25   0.801    -.5673098    .7345839
          F6event |   .2184141   .2600152     0.84   0.401    -.2922081    .7290362
          F5event |  -.4796951   .2637694    -1.82   0.069    -.9976898    .0382995
          F4event |  -.2325114   .1614186    -1.44   0.150    -.5495078     .084485
          F3event |   .0074853   .1524588     0.05   0.961    -.2919157    .3068862
          F2event |   -.059996   .1248273    -0.48   0.631    -.3051339    .1851419
          L0event |   .6092887   .2189284     2.78   0.006     .1793535    1.039224
          L1event |   .9038027   .2273231     3.98   0.000      .457382    1.350223
          L2event |   1.047491   .2631015     3.98   0.000     .5308081    1.564174
          L3event |   .4175165   .2577372     1.62   0.106    -.0886321    .9236651
          L4event |   1.531036   .6502778     2.35   0.019     .2540104    2.808063
          L5event |   2.356691   .5459981     4.32   0.000     1.284451    3.428931
          L6event |  -.3848372    .877942    -0.44   0.661    -2.108954     1.33928
          L7event |   -.503072    .765562    -0.66   0.511    -2.006495    1.000351
          F1event |          0  (omitted)
        ln_ew_ges |   2.465914   1.349016     1.83   0.068    -.1833058    5.115133
         ew_biodt |    .388145   .0296323    13.10   0.000     .3299526    .4463375
        ew_dtmihi |  -.2303872   .0595829    -3.87   0.000     -.347397   -.1133774
         ew_ledig |   .2111155   .0802108     2.63   0.009     .0535962    .3686348
       ew_married |   .2074512   .0799261     2.60   0.010     .0504911    .3644114
        wb_anteil |  -.2425344   .0223631   -10.85   0.000    -.2864513   -.1986174
          wb_ausl |   -.068913   .0145836    -4.73   0.000    -.0975526   -.0402734
         wb_18t24 |  -.0273058   .0275422    -0.99   0.322    -.0813937    .0267821
         wb_25t34 |   .0526598   .0194716     2.70   0.007     .0144212    .0908985
         wb_35t44 |  -.0089296   .0249211    -0.36   0.720    -.0578702    .0400109
         wb_45t59 |   -.038132   .0205362    -1.86   0.064    -.0784614    .0021973
          avg_dur |   .0459808   .0233899     1.97   0.050     .0000472    .0919144
          hh_kids |  -.0642629   .0417916    -1.54   0.125    -.1463339    .0178081
mpreis_flats_rent |  -.0155286   .0234069    -0.66   0.507    -.0614954    .0304383
            _cons |  -11.72708   11.35784    -1.03   0.302     -34.0318    10.57764
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 16 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 3 HDFE groups                           F(  12,    607) =       2.97
Statistics robust to heteroskedasticity           Prob > F        =     0.0005
                                                  R-squared       =     0.9657
                                                  Adj R-squared   =     0.9569
                                                  Within R-sq.    =     0.0177
Number of clusters (sb_new)  =        608         Root MSE        =     1.5998

                               (Std. err. adjusted for 608 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |   .0218905   .3091493     0.07   0.944    -.5852416    .6290227
     F6event |   .0309719   .2902499     0.11   0.915    -.5390439    .6009878
     F5event |  -.4011671   .2445742    -1.64   0.101    -.8814814    .0791472
     F4event |  -.2252061   .1560383    -1.44   0.149    -.5316465    .0812343
     F3event |   .0422346   .1408839     0.30   0.764    -.2344444    .3189135
     F2event |   .0576052   .1114354     0.52   0.605    -.1612405    .2764509
     L0event |   .6382323   .2014918     3.17   0.002     .2425266    1.033938
     L1event |   .8896175   .2235253     3.98   0.000     .4506406    1.328594
     L2event |   .8936715   .2635982     3.39   0.001     .3759962    1.411347
     L3event |   .5172857   .2802849     1.85   0.065    -.0331601    1.067732
     L4event |   1.299288    .794073     1.64   0.102    -.2601762    2.858752
     L5event |   1.968719   .9267441     2.12   0.034     .1487052    3.788733
     L6event |          0  (omitted)
     L7event |          0  (omitted)
     F1event |          0  (omitted)
       _cons |   28.61212   .0434622   658.32   0.000     28.52676    28.69747
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------------------------+
                      Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------------------------+---------------------------------------|
                 wahl_id#stadtbez |       200           1         199     |
                           sb_new |       608         608           0    *|
                          wahl_id |         8           8           0     |
         wahl_id#c.ln_ew_ges_2013 |         8           0           8    ?|
          wahl_id#c.ew_biodt_2013 |         8           0           8    ?|
         wahl_id#c.ew_dtmihi_2013 |         8           0           8    ?|
          wahl_id#c.ew_ledig_2013 |         8           0           8    ?|
        wahl_id#c.ew_married_2013 |         8           0           8    ?|
     wahl_id#c.area_sb_ltw18_2013 |         8           0           8    ?|
         wahl_id#c.wb_anteil_2013 |         8           0           8    ?|
           wahl_id#c.wb_ausl_2013 |         8           8           0    ?|
          wahl_id#c.wb_18t24_2013 |         8           0           8    ?|
          wahl_id#c.wb_25t34_2013 |         8           0           8    ?|
          wahl_id#c.wb_35t44_2013 |         8           0           8    ?|
          wahl_id#c.wb_45t59_2013 |         8           0           8    ?|
           wahl_id#c.avg_dur_2013 |         8           0           8    ?|
           wahl_id#c.hh_kids_2013 |         8           0           8    ?|
 wahl_id#c.mpreis_flats_rent_2013 |         8           0           8    ?|
       wahl_id#c.street_dist_2013 |         8           0           8    ?|
--------------------------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 7 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  14,    617) =       2.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0040
                                                  R-squared       =     0.9828
                                                  Adj R-squared   =     0.9791
                                                  Within R-sq.    =     0.0061
Number of clusters (sb_new)  =        618         Root MSE        =     2.1593

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |  -.1959789   .3602937    -0.54   0.587    -.9035294    .5115717
     F6event |   .1033232   .3138597     0.33   0.742    -.5130396    .7196859
     F5event |  -.6014011   .3238885    -1.86   0.064    -1.237459    .0346563
     F4event |  -.2536044   .2026692    -1.25   0.211    -.6516094    .1444006
     F3event |   .0162015   .1869955     0.09   0.931    -.3510233    .3834262
     F2event |  -.3165656   .1778497    -1.78   0.076    -.6658297    .0326985
     L0event |  -.5691438   .2022959    -2.81   0.005    -.9664159   -.1718718
     L1event |  -.0957661   .2539056    -0.38   0.706    -.5943901    .4028579
     L2event |   .1555893   .3011575     0.52   0.606    -.4358287    .7470073
     L3event |  -.2585508   .3357964    -0.77   0.442    -.9179931    .4008916
     L4event |   .4133635   .7461181     0.55   0.580    -1.051875    1.878602
     L5event |   1.497932   .8788672     1.70   0.089    -.2280014    3.223866
     L6event |   .3132246    .999714     0.31   0.754     -1.65003    2.276479
     L7event |  -1.368432   1.041453    -1.31   0.189    -3.413654    .6767907
     F1event |          0  (omitted)
       _cons |   62.53923   .0525321  1190.50   0.000     62.43606    62.64239
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  18,    617) =      58.26
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9896
                                                  Adj R-squared   =     0.9873
                                                  Within R-sq.    =     0.3979
Number of clusters (sb_new)  =        618         Root MSE        =     1.6816

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |  -.1201362   .3312461    -0.36   0.717    -.7706426    .5303703
     F6event |   .2363343   .2815103     0.84   0.402    -.3165001    .7891688
     F5event |  -.2463456    .267879    -0.92   0.358    -.7724107    .2797195
     F4event |  -.1827307   .1698854    -1.08   0.283    -.5163543     .150893
     F3event |    .026189   .1629482     0.16   0.872    -.2938114    .3461894
     F2event |  -.0041888   .1334087    -0.03   0.975     -.266179    .2578013
     L0event |  -.4362104   .1674331    -2.61   0.009    -.7650181   -.1074026
     L1event |  -.0488243    .210401    -0.23   0.817    -.4620132    .3643646
     L2event |    .221149   .2369102     0.93   0.351    -.2440991    .6863971
     L3event |   .1298716   .2507488     0.52   0.605    -.3625529    .6222961
     L4event |   .5278875   .7047293     0.75   0.454    -.8560714    1.911846
     L5event |   1.502731   .8135983     1.85   0.065    -.0950265    3.100488
     L6event |    .175993   1.051708     0.17   0.867    -1.889367    2.241353
     L7event |  -.0855959   1.209707    -0.07   0.944    -2.461239    2.290047
     F1event |          0  (omitted)
   ln_ew_ges |   1.734216   1.150183     1.51   0.132     -.524532    3.992963
   wb_anteil |  -.5751809   .0241605   -23.81   0.000    -.6226276   -.5277342
    ew_biodt |   .8427706   .0295358    28.53   0.000     .7847679    .9007734
   ew_dtmihi |  -.1509973   .0532244    -2.84   0.005    -.2555202   -.0464743
       _cons |   38.55602   9.842218     3.92   0.000     19.22771    57.88433
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4395
Number of clusters (sb_new)  =        618         Root MSE        =     1.6245

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3259399    -0.25   0.800    -.7224958    .5576764
          F6event |   .2549675   .2761707     0.92   0.356    -.2873811    .7973161
          F5event |  -.2369689   .2619342    -0.90   0.366    -.7513594    .2774217
          F4event |  -.2192898   .1694872    -1.29   0.196    -.5521315     .113552
          F3event |  -.0490231   .1579758    -0.31   0.756    -.3592586    .2612124
          F2event |  -.0537065   .1322374    -0.41   0.685    -.3133964    .2059834
          L0event |  -.3892706   .1633916    -2.38   0.018    -.7101417   -.0683996
          L1event |    .011072    .200952     0.06   0.956    -.3835608    .4057048
          L2event |   .2959665    .224252     1.32   0.187    -.1444233    .7363563
          L3event |   .1243464   .2332215     0.53   0.594    -.3336578    .5823505
          L4event |   .6528057   .6207241     1.05   0.293    -.5661824    1.871794
          L5event |   1.807978   .7132727     2.53   0.011      .407241    3.208714
          L6event |   .6923315   .7952045     0.87   0.384     -.869304    2.253967
          L7event |   .4397263   1.099927     0.40   0.689    -1.720328    2.599781
          F1event |          0  (omitted)
        ln_ew_ges |   1.678109   1.068642     1.57   0.117    -.4205078    3.776725
         ew_biodt |    .761748   .0314252    24.24   0.000     .7000348    .8234612
        ew_dtmihi |  -.1673742   .0522282    -3.20   0.001    -.2699407   -.0648076
         ew_ledig |   .4238555   .0699086     6.06   0.000     .2865679    .5611432
       ew_married |   .6387186   .0688582     9.28   0.000     .5034938    .7739433
        wb_anteil |  -.5320422   .0239517   -22.21   0.000     -.579079   -.4850055
          wb_ausl |  -.0535363   .0175921    -3.04   0.002     -.088084   -.0189885
         wb_18t24 |  -.0438039    .025903    -1.69   0.091    -.0946727    .0070648
         wb_25t34 |  -.0197863   .0166674    -1.19   0.236     -.052518    .0129454
         wb_35t44 |  -.0028286   .0208991    -0.14   0.892    -.0438706    .0382134
         wb_45t59 |  -.0241236   .0196706    -1.23   0.221    -.0627531    .0145059
          avg_dur |   .0170995    .022054     0.78   0.438    -.0262106    .0604096
          hh_kids |  -.1148866    .035755    -3.21   0.001    -.1851028   -.0446704
mpreis_flats_rent |   .0148646   .0236111     0.63   0.529    -.0315033    .0612326
            _cons |   -.366715   10.03193    -0.04   0.971    -20.06759    19.33416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 16 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 3 HDFE groups                           F(  12,    607) =       1.99
Statistics robust to heteroskedasticity           Prob > F        =     0.0227
                                                  R-squared       =     0.9883
                                                  Adj R-squared   =     0.9853
                                                  Within R-sq.    =     0.0060
Number of clusters (sb_new)  =        608         Root MSE        =     1.7839

                               (Std. err. adjusted for 608 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     F7event |   .1935796    .339394     0.57   0.569    -.4729495    .8601087
     F6event |   .2804503   .3121244     0.90   0.369    -.3325245    .8934251
     F5event |  -.0613151   .2941282    -0.21   0.835    -.6389475    .5163173
     F4event |  -.0417554   .1667943    -0.25   0.802    -.3693195    .2858086
     F3event |   .0570993   .1503561     0.38   0.704    -.2381819    .3523805
     F2event |    .121488    .130755     0.93   0.353     -.135299     .378275
     L0event |    -.53788   .1765221    -3.05   0.002    -.8845481   -.1912119
     L1event |   -.018705     .23903    -0.08   0.938    -.4881311    .4507212
     L2event |   .1730798   .2747769     0.63   0.529    -.3665489    .7127085
     L3event |  -.0250384   .3167523    -0.08   0.937    -.6471018    .5970251
     L4event |  -.1566902   .6322795    -0.25   0.804    -1.398411    1.085031
     L5event |   1.182545   .8792254     1.34   0.179    -.5441477    2.909238
     L6event |          0  (omitted)
     L7event |          0  (omitted)
     F1event |          0  (omitted)
       _cons |   63.20344   .0445166  1419.77   0.000     63.11601    63.29086
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------------------------+
                      Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------------------------+---------------------------------------|
                 wahl_id#stadtbez |       200           1         199     |
                           sb_new |       608         608           0    *|
                          wahl_id |         8           8           0     |
         wahl_id#c.ln_ew_ges_2013 |         8           0           8    ?|
          wahl_id#c.ew_biodt_2013 |         8           0           8    ?|
         wahl_id#c.ew_dtmihi_2013 |         8           0           8    ?|
          wahl_id#c.ew_ledig_2013 |         8           0           8    ?|
        wahl_id#c.ew_married_2013 |         8           0           8    ?|
     wahl_id#c.area_sb_ltw18_2013 |         8           0           8    ?|
         wahl_id#c.wb_anteil_2013 |         8           0           8    ?|
           wahl_id#c.wb_ausl_2013 |         8           8           0    ?|
          wahl_id#c.wb_18t24_2013 |         8           0           8    ?|
          wahl_id#c.wb_25t34_2013 |         8           0           8    ?|
          wahl_id#c.wb_35t44_2013 |         8           0           8    ?|
          wahl_id#c.wb_45t59_2013 |         8           0           8    ?|
           wahl_id#c.avg_dur_2013 |         8           0           8    ?|
           wahl_id#c.hh_kids_2013 |         8           0           8    ?|
 wahl_id#c.mpreis_flats_rent_2013 |         8           0           8    ?|
       wahl_id#c.street_dist_2013 |         8           0           8    ?|
--------------------------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         
.         * TABLE C5. Robustness to Inclusion of Covariates–Pooled Reassignments
.         outreg using "$tables/Table_C5_ES_rob_covariates_bsl", replay tex replace fragment note(
> "some:`somectr_1'; all: $ctr; interacted: $intctr")
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_C5
> _ES_rob_covariates_bsl.tex not found)
 -----------------------------------------------------------------------------------------------
                                             (1)       (2)              (3)             (4)    
                                                               Polling place turnout           
 -----------------------------------------------------------------------------------------------
  Reassignment (#t-4#)                      -0.05     -0.02            0.01             0.18   
                                            (0.19)    (0.18)          (0.17)           (0.18)  
  Reassignment (#t-3#)                      -0.08     -0.08            -0.06            0.01   
                                            (0.18)    (0.17)          (0.17)           (0.17)  
  Reassignment (#t-2#)                      -0.13      0.03            0.01             0.06   
                                            (0.14)    (0.12)          (0.12)           (0.13)  
  Reassignment (#t+0#)                     -1.12***  -1.06***        -1.00***         -1.18*** 
                                            (0.24)    (0.24)          (0.23)           (0.23)  
  Reassignment (#t+1#)                     -0.98***  -0.97***        -0.89***         -0.91*** 
                                            (0.25)    (0.24)          (0.23)           (0.25)  
  Reassignment (#t+2#)                     -0.75**   -0.77**          -0.75**          -0.72*  
                                            (0.28)    (0.27)          (0.26)           (0.29)  
  R2                                         0.97      0.97            0.97             0.97   
  N                                         4,666     4,666            4,666           4,609   
  Time-varying controls                     none      some             all             none    
  Pre-treatment covariates x election FE                                                yes    
 -----------------------------------------------------------------------------------------------
some:ln_ew_ges wb_anteil ew_biodt ew_dtmihi ; all: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_marrie
> d wb_anteil                                  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 avg_dur
>  hh_kids mpreis_flats_rent; interacted: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_married area_sb
> _ltw18                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 a
> vg_dur hh_kids mpreis_flats_rent                                        street_dist


 -----------------------------------------------------------------------------------------------
                                             (5)      (6)          (7)          (8)      (9)   
                                                             Mail-in turnout                   
 -----------------------------------------------------------------------------------------------
  Reassignment (#t-4#)                      -0.20    -0.17        -0.23        -0.23    -0.25  
                                           (0.18)   (0.16)       (0.16)       (0.16)   (0.20)  
  Reassignment (#t-3#)                      0.10     0.10         0.01         0.04     0.02   
                                           (0.16)   (0.16)       (0.15)       (0.14)   (0.19)  
  Reassignment (#t-2#)                      -0.18    -0.03        -0.06        0.06     -0.32  
                                           (0.13)   (0.13)       (0.12)       (0.11)   (0.18)  
  Reassignment (#t+0#)                      0.55*   0.62**       0.61**       0.64**   -0.57** 
                                           (0.23)   (0.22)       (0.22)       (0.20)   (0.20)  
  Reassignment (#t+1#)                     0.88***  0.93***      0.90***      0.89***   -0.10  
                                           (0.24)   (0.23)       (0.23)       (0.22)   (0.25)  
  Reassignment (#t+2#)                     0.90**   0.99***      1.05***      0.89***   0.16   
                                           (0.29)   (0.27)       (0.26)       (0.26)   (0.30)  
  R2                                        0.95     0.96         0.96         0.97     0.98   
  N                                         4,666    4,666        4,666        4,609    4,666  
  Time-varying controls                     none     some         all          none     none   
  Pre-treatment covariates x election FE                                       yes             
 -----------------------------------------------------------------------------------------------
some:ln_ew_ges wb_anteil ew_biodt ew_dtmihi ; all: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_marrie
> d wb_anteil                                  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 avg_dur
>  hh_kids mpreis_flats_rent; interacted: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_married area_sb
> _ltw18                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 a
> vg_dur hh_kids mpreis_flats_rent                                        street_dist


           ---------------------------------------------------------------------------
                                                      (10)        (11)        (12)   
                                                              Total turnout          
           ---------------------------------------------------------------------------
            Reassignment (#t-4#)                      -0.18       -0.22       -0.04  
                                                     (0.17)      (0.17)      (0.17)  
            Reassignment (#t-3#)                      0.03        -0.05       0.06   
                                                     (0.16)      (0.16)      (0.15)  
            Reassignment (#t-2#)                      -0.00       -0.05       0.12   
                                                     (0.13)      (0.13)      (0.13)  
            Reassignment (#t+0#)                     -0.44**     -0.39*      -0.54** 
                                                     (0.17)      (0.16)      (0.18)  
            Reassignment (#t+1#)                      -0.05       0.01        -0.02  
                                                     (0.21)      (0.20)      (0.24)  
            Reassignment (#t+2#)                      0.22        0.30        0.17   
                                                     (0.24)      (0.22)      (0.27)  
            R2                                        0.99        0.99        0.99   
            N                                         4,666       4,666       4,609  
            Time-varying controls                     some        all         none   
            Pre-treatment covariates x election FE                            yes    
           ---------------------------------------------------------------------------
some:ln_ew_ges wb_anteil ew_biodt ew_dtmihi ; all: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_marrie
> d wb_anteil                                  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 avg_dur
>  hh_kids mpreis_flats_rent; interacted: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_married area_sb
> _ltw18                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 a
> vg_dur hh_kids mpreis_flats_rent                                        street_dist


.         cleantex     "$tables/Table_C5_ES_rob_covariates_bsl.tex"  ,  replace

\begin{tabular}{lcccccccccccc}
\toprule  & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12)\\
 &  &  & Polling place turnout &  &  &  & Mail-in turnout &  &  &  & Total turnout & \\
 Reassignment ($ t-4$) & -0.05 & -0.02 & 0.01 & 0.18 & -0.20 & -0.17 & -0.23 & -0.23 & -0.25 & -0.
> 18 & -0.22 & -0.04\\
 & (0.19) & (0.18) & (0.17) & (0.18) & (0.18) & (0.16) & (0.16) & (0.16) & (0.20) & (0.17) & (0.17
> ) & (0.17)\\
Reassignment ($ t-3$) & -0.08 & -0.08 & -0.06 & 0.01 & 0.10 & 0.10 & 0.01 & 0.04 & 0.02 & 0.03 & -
> 0.05 & 0.06\\
 & (0.18) & (0.17) & (0.17) & (0.17) & (0.16) & (0.16) & (0.15) & (0.14) & (0.19) & (0.16) & (0.16
> ) & (0.15)\\
Reassignment ($ t-2$) & -0.13 & 0.03 & 0.01 & 0.06 & -0.18 & -0.03 & -0.06 & 0.06 & -0.32 & -0.00 
> & -0.05 & 0.12\\
 & (0.14) & (0.12) & (0.12) & (0.13) & (0.13) & (0.13) & (0.12) & (0.11) & (0.18) & (0.13) & (0.13
> ) & (0.13)\\
Reassignment ($ t+0$) & -1.12*** & -1.06*** & -1.00*** & -1.18*** & 0.55* & 0.62** & 0.61** & 0.64
> ** & -0.57** & -0.44** & -0.39* & -0.54**\\
 & (0.24) & (0.24) & (0.23) & (0.23) & (0.23) & (0.22) & (0.22) & (0.20) & (0.20) & (0.17) & (0.16
> ) & (0.18)\\
Reassignment ($ t+1$) & -0.98*** & -0.97*** & -0.89*** & -0.91*** & 0.88*** & 0.93*** & 0.90*** & 
> 0.89*** & -0.10 & -0.05 & 0.01 & -0.02\\
 & (0.25) & (0.24) & (0.23) & (0.25) & (0.24) & (0.23) & (0.23) & (0.22) & (0.25) & (0.21) & (0.20
> ) & (0.24)\\
Reassignment ($ t+2$) & -0.75** & -0.77** & -0.75** & -0.72* & 0.90** & 0.99*** & 1.05*** & 0.89**
> * & 0.16 & 0.22 & 0.30 & 0.17\\
 & (0.28) & (0.27) & (0.26) & (0.29) & (0.29) & (0.27) & (0.26) & (0.26) & (0.30) & (0.24) & (0.22
> ) & (0.27)\\
$ R^2$  & 0.97 & 0.97 & 0.97 & 0.97 & 0.95 & 0.96 & 0.96 & 0.97 & 0.98 & 0.99 & 0.99 & 0.99\\
Observations & 4,666 & 4,666 & 4,666 & 4,609 & 4,666 & 4,666 & 4,666 & 4,609 & 4,666 & 4,666 & 4,6
> 66 & 4,609\\
Time-varying controls & none  & some  & all  & none  & none  & some  & all  & none  & none  & some
>   & all  & none \\
Pre-treatment covariates x election FE &  &  &  & yes  &  &  &  & yes  &  &  &  & yes \\
\bottomrule\end{tabular}
\smallskipsome:ln_ew_ges wb_anteil ew_biodt ew_dtmihi ; all: ln_ew_ges ew_biodt ew_dtmihi ew_ledig
>  ew_married wb_anteil                                  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t
> 59 avg_dur hh_kids mpreis_flats_rent; interacted: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_marri
> ed area_sb_ltw18                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 
> wb_45t59 avg_dur hh_kids mpreis_flats_rent                                        street_dist\\
\smallskip


.         
. ********************************************************************************
.                 // Effects by distance: Robustness to inclusion of covariates //
. ********************************************************************************                
.                 
.         
.         // ORDER dummies
.         order *event_b, last

.         order F1event*,last     

.         
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         outreg,clear

.         
.         local j=0 // column counter

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.         // No Controls
.         local j `++j' 
  3.                  reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b /*$
> ctr*/ $wgt ///
>                                         if  smpl_trim==1 , absorb(i.wahl_id#i.stadtbez i.sb_new)
>  cluster(sb_new)
  4.                                  qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2eve
> nt_b) ctitle("","(`j')" \ "", "") ///
>                                         addrow(Time-varying controls, none)
  5. 
. 
.                 estimates store `v'_a_noctr
  6.                 estimates store `v'_b_noctr     
  7.                 
.                 
.         // SOME TIME-VARYING CONTROLS (not in graph output only table)                          
.         local somectr_1= "ln_ew_ges wb_anteil ew_biodt ew_dtmihi " 
  8.         local j `++j' 
  9.                  reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b `so
> mectr_1' $wgt ///
>                                         if  smpl_trim==1 , absorb(i.wahl_id#i.stadtbez i.sb_new)
>  cluster(sb_new)
 10.                                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2even
> t_b) ctitle("","(`j')" \ "", "") addrow(Time-varying controls, some)
 11. 
. 
.                 estimates store `v'_a_noctr
 12.                 estimates store `v'_b_noctr                     
 13.                 
.                 
.         // ALL TIME-VARYING CONTROLS (Baseline)
.         local j `++j'
 14.                  reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b $ct
> r $wgt ///
>                                         if  smpl_trim==1  , absorb(i.wahl_id#i.stadtbez i.sb_new
> ) cluster(sb_new)
 15.                                  qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2eve
> nt_b)  ctitle("", "(`j')" \"", "`:var lab `v''") ///
>                                         addrow(Time-varying controls,all)
 16. 
. 
.                 estimates store `v'_a_bsl
 17.                 estimates store `v'_b_bsl
 18.                         
.         
.         // INTERACTTION: pre-treatment control x election FE (excluding treated units in 2013); 
> NO time-varying controls
.         local j `++j'
 19.                  reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b /*$
> ctr*/ $wgt ///
>                                         if  smpl_trim==1 & Ei!=1 , absorb(i.wahl_id#i.stadtbez i
> .sb_new c.(*_2013)##i.wahl_id) cluster(sb_new) 
 20.                                 qui  outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2eve
> nt_b) ctitle("","(`j')" \ "","") ///
>                                         addrow(Time-varying controls, none \ pre-treatment covar
> iates x election FE, yes)
 21. 
. 
.                 estimates store `v'_a_int
 22.                 estimates store `v'_b_int       
 23.         }               
(MWFE estimator converged in 7 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =       4.99
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9687
                                                  Adj R-squared   =     0.9618
                                                  Within R-sq.    =     0.0713
Number of clusters (sb_new)  =        618         Root MSE        =     1.7951

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |   .2798921   .6392228     0.44   0.662     -.975424    1.535208
   F6event_a |  -.0881045   .5923312    -0.15   0.882    -1.251334    1.075125
   F5event_a |     .07587   .4643349     0.16   0.870    -.8359984    .9877384
   F4event_a |  -.2519534   .2538928    -0.99   0.321    -.7505521    .2466454
   F3event_a |  -.3694404   .2419303    -1.53   0.127     -.844547    .1056662
   F2event_a |  -.5559464   .2114109    -2.63   0.009    -.9711187   -.1407742
   L0event_a |    .417286   .3607384     1.16   0.248    -.2911379     1.12571
   L1event_a |   .6542171   .3400819     1.92   0.055    -.0136412    1.322076
   L2event_a |   .5425293   .3769319     1.44   0.151    -.1976957    1.282754
   L3event_a |   .4899706   .3576693     1.37   0.171    -.2124262    1.192367
   L4event_a |  -.0265918   .6581653    -0.04   0.968    -1.319108    1.265924
   L5event_a |  -.0445382   .8377557    -0.05   0.958    -1.689737     1.60066
   L6event_a |   2.655289   1.464928     1.81   0.070    -.2215593    5.532138
   L7event_a |  -.5003325   .5551233    -0.90   0.368    -1.590493    .5898276
   F7event_b |  -.5847669   .4446239    -1.32   0.189    -1.457927    .2883929
   F6event_b |  -.0834786   .3746973    -0.22   0.824    -.8193152    .6523581
   F5event_b |  -.0155407   .3338586    -0.05   0.963    -.6711777    .6400964
   F4event_b |   .0566714   .2231214     0.25   0.800    -.3814981    .4948408
   F3event_b |   .0847383   .2192453     0.39   0.699    -.3458193    .5152958
   F2event_b |   .1459222   .1755463     0.83   0.406    -.1988184    .4906628
   L0event_b |  -2.049447   .2762334    -7.42   0.000    -2.591919   -1.506976
   L1event_b |   -2.15023   .2817979    -7.63   0.000    -2.703629   -1.596831
   L2event_b |  -1.625018   .3344358    -4.86   0.000    -2.281788   -.9682475
   L3event_b |  -1.099787    .326241    -3.37   0.001    -1.740464   -.4591095
   L4event_b |  -1.434487   .6537329    -2.19   0.029    -2.718298   -.1506753
   L5event_b |  -.9618012   .5991108    -1.61   0.109    -2.138345    .2147424
   L6event_b |  -.5883283   .9274608    -0.63   0.526    -2.409691    1.233034
   L7event_b |   .9385555   1.839085     0.51   0.610    -2.673069     4.55018
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
       _cons |   34.09954   .0545326   625.31   0.000     33.99245    34.20664
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  32,    617) =      15.34
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9723
                                                  Adj R-squared   =     0.9662
                                                  Within R-sq.    =     0.1797
Number of clusters (sb_new)  =        618         Root MSE        =     1.6880

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |   .3068575   .5984916     0.51   0.608    -.8684701    1.482185
   F6event_a |  -.0266228   .5671423    -0.05   0.963    -1.140386     1.08714
   F5event_a |   .2233807   .4093561     0.55   0.585    -.5805195    1.027281
   F4event_a |  -.1962814    .246591    -0.80   0.426    -.6805408    .2879779
   F3event_a |  -.2965661    .241118    -1.23   0.219    -.7700776    .1769453
   F2event_a |  -.1904741   .1804396    -1.06   0.292    -.5448243    .1638762
   L0event_a |   .4574102   .3516819     1.30   0.194    -.2332285    1.148049
   L1event_a |   .6107723   .3283295     1.86   0.063    -.0340065    1.255551
   L2event_a |    .566398   .3525057     1.61   0.109    -.1258584    1.258654
   L3event_a |   .8080059    .379117     2.13   0.033     .0634898    1.552522
   L4event_a |   .2149575   .6958092     0.31   0.757    -1.151484    1.581399
   L5event_a |   .1551387    1.02968     0.15   0.880    -1.866964    2.177242
   L6event_a |   2.570426    1.43905     1.79   0.075    -.2556032    5.396454
   L7event_a |  -.2129955   .5418996    -0.39   0.694    -1.277187    .8511956
   F7event_b |  -.5786468   .4207949    -1.38   0.170    -1.405011     .247717
   F6event_b |  -.0627895   .3615999    -0.17   0.862    -.7729053    .6473263
   F5event_b |   .1435869    .299985     0.48   0.632    -.4455285    .7327023
   F4event_b |   .0812306    .215599     0.38   0.706    -.3421662    .5046274
   F3event_b |   .0506593   .2109713     0.24   0.810    -.3636496    .4649682
   F2event_b |   .1752131   .1469049     1.19   0.233    -.1132812    .4637073
   L0event_b |   -1.97372    .270122    -7.31   0.000     -2.50419    -1.44325
   L1event_b |  -2.108163   .2714301    -7.77   0.000    -2.641202   -1.575124
   L2event_b |    -1.6765    .322878    -5.19   0.000    -2.310573   -1.042427
   L3event_b |  -1.088681   .3377765    -3.22   0.001    -1.752012   -.4253502
   L4event_b |  -1.465177   .5270807    -2.78   0.006    -2.500267   -.4300877
   L5event_b |  -1.132049    .624512    -1.81   0.070    -2.358476    .0943774
   L6event_b |  -.6959918   .6974335    -1.00   0.319    -2.065623    .6736394
   L7event_b |   1.649904   1.592439     1.04   0.301    -1.477353    4.777161
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
   ln_ew_ges |  -.6785353   .7650237    -0.89   0.375    -2.180901    .8238307
   wb_anteil |  -.2849979   .0196484   -14.50   0.000    -.3235837   -.2464122
    ew_biodt |   .4002432   .0250602    15.97   0.000     .3510296    .4494568
   ew_dtmihi |   .1030216     .04924     2.09   0.037     .0063233    .1997199
       _cons |   32.62884   6.720585     4.86   0.000     19.43084    45.82683
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      14.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9734
                                                  Adj R-squared   =     0.9674
                                                  Within R-sq.    =     0.2120
Number of clusters (sb_new)  =        618         Root MSE        =     1.6565

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .3228052   .5620586     0.57   0.566    -.7809746    1.426585
        F6event_a |   -.008119   .5445209    -0.01   0.988    -1.077458     1.06122
        F5event_a |   .2401916    .384451     0.62   0.532    -.5147995    .9951827
        F4event_a |   -.156618   .2415581    -0.65   0.517    -.6309938    .3177578
        F3event_a |  -.2497959    .237662    -1.05   0.294    -.7165204    .2169286
        F2event_a |  -.2004224   .1774212    -1.13   0.259    -.5488451    .1480002
        L0event_a |    .475771   .3448323     1.38   0.168    -.2014161    1.152958
        L1event_a |   .6029534   .3142192     1.92   0.055    -.0141153    1.220022
        L2event_a |    .489146   .3473889     1.41   0.160     -.193062    1.171354
        L3event_a |    .768641   .3520682     2.18   0.029     .0772438    1.460038
        L4event_a |   .2930804   .6191775     0.47   0.636    -.9228704    1.509031
        L5event_a |   .5968234   1.001193     0.60   0.551    -1.369335    2.562982
        L6event_a |   2.971707   1.125918     2.64   0.009     .7606118    5.182803
        L7event_a |   .4542621    .595416     0.76   0.446    -.7150254     1.62355
        F7event_b |   -.458259   .4061302    -1.13   0.260    -1.255824    .3393062
        F6event_b |   .0370394   .3490613     0.11   0.916    -.6484527    .7225316
        F5event_b |   .1948103   .2958098     0.66   0.510    -.3861058    .7757263
        F4event_b |   .1010004   .2103875     0.48   0.631     -.312162    .5141628
        F3event_b |    .051904   .2046495     0.25   0.800    -.3499901     .453798
        F2event_b |   .1484136   .1455165     1.02   0.308    -.1373541    .4341812
        L0event_b |  -1.892486   .2685797    -7.05   0.000    -2.419927   -1.365045
        L1event_b |  -1.964155   .2722347    -7.21   0.000    -2.498774   -1.429536
        L2event_b |   -1.59154    .310441    -5.13   0.000    -2.201189   -.9818908
        L3event_b |  -1.046249   .3334843    -3.14   0.002    -1.701151   -.3913475
        L4event_b |  -1.513942   .5864355    -2.58   0.010    -2.665594   -.3622908
        L5event_b |  -1.192543   .6858851    -1.74   0.083    -2.539496     .154409
        L6event_b |  -.5363127   .5611732    -0.96   0.340    -1.638354    .5657283
        L7event_b |   1.781413   1.393593     1.28   0.202    -.9553482    4.518174
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -.9961214   .9212525    -1.08   0.280    -2.805292    .8130493
         ew_biodt |   .3683074   .0277532    13.27   0.000     .3138051    .4228097
        ew_dtmihi |   .0672716   .0499711     1.35   0.179    -.0308624    .1654056
         ew_ledig |   .2075291   .0540848     3.84   0.000     .1013165    .3137417
       ew_married |   .4088781   .0556124     7.35   0.000     .2996655    .5180906
        wb_anteil |   -.284197   .0201233   -14.12   0.000    -.3237154   -.2446785
          wb_ausl |    .016981   .0158374     1.07   0.284    -.0141208    .0480829
         wb_18t24 |  -.0166004   .0296128    -0.56   0.575    -.0747545    .0415537
         wb_25t34 |  -.0623142   .0187354    -3.33   0.001    -.0991071   -.0255213
         wb_35t44 |   .0057088   .0225147     0.25   0.800    -.0385059    .0499234
         wb_45t59 |   .0149036   .0214535     0.69   0.488    -.0272271    .0570344
          avg_dur |  -.0226264   .0201538    -1.12   0.262    -.0622046    .0169519
          hh_kids |  -.0391984   .0390032    -1.01   0.315    -.1157936    .0373968
mpreis_flats_rent |   .0286627   .0245902     1.17   0.244     -.019628    .0769534
            _cons |   13.43092   8.531997     1.57   0.116    -3.324354     30.1862
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 16 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 3 HDFE groups                           F(  24,    607) =       6.34
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9748
                                                  Adj R-squared   =     0.9683
                                                  Within R-sq.    =     0.0849
Number of clusters (sb_new)  =        608         Root MSE        =     1.6374

                               (Std. err. adjusted for 608 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |   .6456795   .5818634     1.11   0.268    -.4970303    1.788389
   F6event_a |    .323388   .5631975     0.57   0.566    -.7826643     1.42944
   F5event_a |   .2913575   .4092099     0.71   0.477    -.5122815    1.094997
   F4event_a |   .0646894   .2530432     0.26   0.798     -.432257    .5616358
   F3event_a |  -.0842992   .2057725    -0.41   0.682    -.4884116    .3198132
   F2event_a |  -.0726672    .175927    -0.41   0.680    -.4181667    .2728324
   L0event_a |   .4839083    .327008     1.48   0.139     -.158296    1.126113
   L1event_a |   1.022995   .3451475     2.96   0.003     .3451669    1.700823
   L2event_a |   .8624807   .3940994     2.19   0.029     .0885169    1.636444
   L3event_a |   .2351666   .3565614     0.66   0.510     -.465077    .9354103
   L4event_a |  -.3918393   .6499289    -0.60   0.547    -1.668222     .884543
   L5event_a |   .4490704   1.148636     0.39   0.696    -1.806713    2.704854
   L6event_a |          0  (omitted)
   L7event_a |          0  (omitted)
   F7event_b |  -.3165353    .437194    -0.72   0.469    -1.175132    .5420612
   F6event_b |   .0380051   .3585691     0.11   0.916    -.6661816    .7421918
   F5event_b |   .1794284   .3367085     0.53   0.594    -.4818266    .8406835
   F4event_b |    .175828   .2099376     0.84   0.403    -.2364642    .5881203
   F3event_b |   .0603352   .2087193     0.29   0.773    -.3495644    .4702348
   F2event_b |    .146472   .1489822     0.98   0.326    -.1461111    .4390552
   L0event_b |  -2.106103   .2644695    -7.96   0.000    -2.625489   -1.586716
   L1event_b |  -2.235633   .2745438    -8.14   0.000    -2.774804   -1.696462
   L2event_b |   -1.75048   .3342122    -5.24   0.000    -2.406833   -1.094128
   L3event_b |  -.9413016   .3243897    -2.90   0.004    -1.578364   -.3042392
   L4event_b |  -1.665177   .9568972    -1.74   0.082    -3.544409    .2140536
   L5event_b |  -1.584787   .4561374    -3.47   0.001    -2.480586   -.6889879
   L6event_b |          0  (omitted)
   L7event_b |          0  (omitted)
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
       _cons |   34.06913   .0520359   654.72   0.000     33.96694    34.17132
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------------------------+
                      Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------------------------+---------------------------------------|
                 wahl_id#stadtbez |       200           1         199     |
                           sb_new |       608         608           0    *|
                          wahl_id |         8           8           0     |
         wahl_id#c.ln_ew_ges_2013 |         8           0           8    ?|
          wahl_id#c.ew_biodt_2013 |         8           0           8    ?|
         wahl_id#c.ew_dtmihi_2013 |         8           0           8    ?|
          wahl_id#c.ew_ledig_2013 |         8           0           8    ?|
        wahl_id#c.ew_married_2013 |         8           0           8    ?|
     wahl_id#c.area_sb_ltw18_2013 |         8           0           8    ?|
         wahl_id#c.wb_anteil_2013 |         8           0           8    ?|
           wahl_id#c.wb_ausl_2013 |         8           8           0    ?|
          wahl_id#c.wb_18t24_2013 |         8           0           8    ?|
          wahl_id#c.wb_25t34_2013 |         8           0           8    ?|
          wahl_id#c.wb_35t44_2013 |         8           0           8    ?|
          wahl_id#c.wb_45t59_2013 |         8           0           8    ?|
           wahl_id#c.avg_dur_2013 |         8           0           8    ?|
           wahl_id#c.hh_kids_2013 |         8           0           8    ?|
 wahl_id#c.mpreis_flats_rent_2013 |         8           0           8    ?|
       wahl_id#c.street_dist_2013 |         8           0           8    ?|
--------------------------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 7 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =       7.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9537
                                                  Adj R-squared   =     0.9434
                                                  Within R-sq.    =     0.0396
Number of clusters (sb_new)  =        618         Root MSE        =     1.8506

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |  -.7993511   .5094606    -1.57   0.117    -1.799838    .2011359
   F6event_a |  -.2668545   .4967495    -0.54   0.591    -1.242379    .7086702
   F5event_a |  -1.235003   .4248857    -2.91   0.004      -2.0694   -.4006054
   F4event_a |  -.2088971   .2183357    -0.96   0.339    -.6376684    .2198742
   F3event_a |   .1933861   .2443404     0.79   0.429    -.2864535    .6732257
   F2event_a |  -.4022072   .1904853    -2.11   0.035    -.7762854   -.0281291
   L0event_a |  -.4230799   .3300741    -1.28   0.200    -1.071285    .2251249
   L1event_a |  -.3362429   .3331804    -1.01   0.313     -.990548    .3180621
   L2event_a |   -.192068   .3918992    -0.49   0.624    -.9616861      .57755
   L3event_a |  -1.013085   .4205416    -2.41   0.016    -1.838952   -.1872186
   L4event_a |  -.3098231   .9663454    -0.32   0.749    -2.207548    1.587902
   L5event_a |   1.264652   .8463784     1.49   0.136    -.3974793    2.926784
   L6event_a |  -2.828623   .7267598    -3.89   0.000    -4.255846   -1.401401
   L7event_a |   -1.99091   .7180136    -2.77   0.006    -3.400957   -.5808635
   F7event_b |    .566995   .3449805     1.64   0.101    -.1104834    1.244473
   F6event_b |   .4193303   .2986575     1.40   0.161    -.1671782    1.005839
   F5event_b |  -.3457037   .3291733    -1.05   0.294    -.9921396    .3007323
   F4event_b |  -.1914348    .220094    -0.87   0.385    -.6236591    .2407894
   F3event_b |    .036875    .187667     0.20   0.844    -.3316685    .4054185
   F2event_b |  -.0794131   .1523195    -0.52   0.602    -.3785407    .2197145
   L0event_b |    1.12241   .2686664     4.18   0.000     .5947983    1.650021
   L1event_b |   1.720736    .290674     5.92   0.000     1.149905    2.291566
   L2event_b |   1.635336   .3676595     4.45   0.000     .9133206    2.357352
   L3event_b |   1.073822   .4142759     2.59   0.010     .2602603    1.887384
   L4event_b |   2.795979   .5610074     4.98   0.000     1.694263    3.897694
   L5event_b |   2.645987   .8979648     2.95   0.003     .8825492    4.409425
   L6event_b |   1.406213   .6706402     2.10   0.036     .0891985    2.723227
   L7event_b |  -1.056183   .8349744    -1.26   0.206    -2.695919    .5835534
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
       _cons |   28.44206   .0490831   579.47   0.000     28.34567    28.53845
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  32,    617) =      16.51
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9618
                                                  Adj R-squared   =     0.9533
                                                  Within R-sq.    =     0.2087
Number of clusters (sb_new)  =        618         Root MSE        =     1.6806

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |  -.7060064    .553758    -1.27   0.203    -1.793485    .3814726
   F6event_a |  -.1018949   .4778992    -0.21   0.831    -1.040401    .8366112
   F5event_a |  -1.002288   .4032741    -2.49   0.013    -1.794244   -.2103315
   F4event_a |  -.1338943   .2195972    -0.61   0.542    -.5651429    .2973542
   F3event_a |   .2913299   .2245435     1.30   0.195    -.1496324    .7322921
   F2event_a |  -.0187624   .1823095    -0.10   0.918    -.3767848    .3392599
   L0event_a |  -.3896247   .3061073    -1.27   0.204    -.9907632    .2115138
   L1event_a |  -.3495492   .3102684    -1.13   0.260    -.9588593    .2597609
   L2event_a |  -.0371816    .363949    -0.10   0.919    -.7519106    .6775473
   L3event_a |  -.6794764   .3409357    -1.99   0.047    -1.349011   -.0099413
   L4event_a |  -.1517284   1.017769    -0.15   0.882     -2.15044    1.846983
   L5event_a |   1.478076   .9283209     1.59   0.112    -.3449752    3.301128
   L6event_a |  -3.073871    .781539    -3.93   0.000     -4.60867   -1.539071
   L7event_a |  -1.897185   .5776577    -3.28   0.001    -3.031599   -.7627716
   F7event_b |    .617468   .3266885     1.89   0.059    -.0240882    1.259024
   F6event_b |   .4813201   .2735349     1.76   0.079    -.0558522    1.018492
   F5event_b |  -.1736598   .3144463    -0.55   0.581    -.7911747     .443855
   F4event_b |  -.1743419   .1925327    -0.91   0.366    -.5524408     .203757
   F3event_b |  -.0073546   .1826566    -0.04   0.968    -.3660586    .3513493
   F2event_b |  -.0616819   .1486044    -0.42   0.678    -.3535137    .2301499
   L0event_b |   1.232152   .2567735     4.80   0.000     .7278966    1.736408
   L1event_b |    1.82104   .2678925     6.80   0.000     1.294949    2.347132
   L2event_b |   1.691415   .3321253     5.09   0.000     1.039181    2.343648
   L3event_b |   1.256743   .3347257     3.75   0.000     .5994029    1.914082
   L4event_b |   2.690114   .4965413     5.42   0.000     1.714998     3.66523
   L5event_b |   2.569092   .5179928     4.96   0.000      1.55185    3.586335
   L6event_b |    1.63009    .723174     2.25   0.025      .209909    3.050271
   L7event_b |   .4098143   1.234931     0.33   0.740    -2.015363    2.834992
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
   ln_ew_ges |   2.391676   1.314849     1.82   0.069    -.1904461    4.973798
   wb_anteil |  -.2869716   .0233227   -12.30   0.000    -.3327731     -.24117
    ew_biodt |   .4388632   .0276673    15.86   0.000     .3845297    .4931967
   ew_dtmihi |  -.2564333   .0528143    -4.86   0.000    -.3601508   -.1527158
       _cons |   6.134415   11.01724     0.56   0.578    -15.50142    27.77025
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      14.90
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9628
                                                  Adj R-squared   =     0.9544
                                                  Within R-sq.    =     0.2284
Number of clusters (sb_new)  =        618         Root MSE        =     1.6618

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.7726385   .5349539    -1.44   0.149     -1.82319    .2779128
        F6event_a |  -.1638495     .45516    -0.36   0.719      -1.0577    .7300012
        F5event_a |  -1.068847   .4042341    -2.64   0.008    -1.862688   -.2750054
        F4event_a |  -.2732057   .2186614    -1.25   0.212    -.7026165    .1562052
        F3event_a |   .1422311   .2194149     0.65   0.517    -.2886594    .5731216
        F2event_a |  -.0493139    .179667    -0.27   0.784    -.4021469    .3035192
        L0event_a |  -.4599664   .3083689    -1.49   0.136    -1.065546    .1456135
        L1event_a |  -.3946958   .3108984    -1.27   0.205    -1.005243    .2158516
        L2event_a |   .0490141   .3556253     0.14   0.890    -.6493687    .7473968
        L3event_a |  -.6600526   .3220275    -2.05   0.041    -1.292455   -.0276498
        L4event_a |  -.1714753   .9749698    -0.18   0.860    -2.086137    1.743186
        L5event_a |   1.330135   .8766554     1.52   0.130    -.3914553    3.051725
        L6event_a |  -2.832304   .6886765    -4.11   0.000    -4.184738    -1.47987
        L7event_a |  -1.841205   .6807652    -2.70   0.007    -3.178103   -.5043073
        F7event_b |   .5841825   .3298669     1.77   0.077    -.0636156    1.231981
        F6event_b |   .4327823   .2632287     1.64   0.101    -.0841505     .949715
        F5event_b |  -.1844277   .3115985    -0.59   0.554    -.7963498    .4274945
        F4event_b |  -.1933853   .1940332    -1.00   0.319    -.5744308    .1876602
        F3event_b |  -.0668334   .1799966    -0.37   0.711    -.4203135    .2866468
        F2event_b |  -.0883951   .1461363    -0.60   0.545      -.37538    .1985897
        L0event_b |   1.258426   .2583978     4.87   0.000     .7509797    1.765871
        L1event_b |   1.819219   .2672851     6.81   0.000      1.29432    2.344118
        L2event_b |   1.723036   .3262228     5.28   0.000     1.082394    2.363677
        L3event_b |   1.207218   .3296335     3.66   0.000     .5598788    1.854558
        L4event_b |   2.900775    .518063     5.60   0.000     1.883394    3.918155
        L5event_b |    2.93048   .5822414     5.03   0.000     1.787065    4.073896
        L6event_b |   1.816891   .6623496     2.74   0.006      .516158    3.117624
        L7event_b |    .546884   1.228547     0.45   0.656    -1.865757    2.959525
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.622643   1.331899     1.97   0.049      .007038    5.238248
         ew_biodt |    .390918   .0287073    13.62   0.000     .3345422    .4472938
        ew_dtmihi |  -.2363622   .0597435    -3.96   0.000    -.3536875   -.1190369
         ew_ledig |   .2132309   .0770102     2.77   0.006      .061997    .3644649
       ew_married |   .2221102   .0771453     2.88   0.004      .070611    .3736095
        wb_anteil |  -.2460859   .0218023   -11.29   0.000    -.2889017   -.2032702
          wb_ausl |  -.0699769    .014375    -4.87   0.000    -.0982067   -.0417471
         wb_18t24 |  -.0295899   .0273374    -1.08   0.279    -.0832755    .0240957
         wb_25t34 |   .0438136   .0190773     2.30   0.022     .0063493    .0812779
         wb_35t44 |  -.0081487    .024216    -0.34   0.737    -.0557044     .039407
         wb_45t59 |  -.0389038    .020268    -1.92   0.055    -.0787064    .0008987
          avg_dur |   .0429859   .0224788     1.91   0.056    -.0011584    .0871302
          hh_kids |  -.0731669   .0409186    -1.79   0.074    -.1535236    .0071898
mpreis_flats_rent |  -.0170848   .0235218    -0.73   0.468    -.0632773    .0291077
            _cons |  -12.97282   11.12696    -1.17   0.244    -34.82413     8.87849
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 16 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 3 HDFE groups                           F(  24,    607) =       4.00
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9672
                                                  Adj R-squared   =     0.9587
                                                  Within R-sq.    =     0.0455
Number of clusters (sb_new)  =        608         Root MSE        =     1.5816

                               (Std. err. adjusted for 608 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_po~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |    -.61124   .4369567    -1.40   0.162     -1.46937    .2468904
   F6event_a |  -.2934084   .4539974    -0.65   0.518    -1.185005    .5981879
   F5event_a |  -.6457538   .3800217    -1.70   0.090    -1.392071    .1005633
   F4event_a |  -.1669112   .1994896    -0.84   0.403    -.5586847    .2248624
   F3event_a |   .0458672   .1873307     0.24   0.807    -.3220278    .4137623
   F2event_a |  -.0931508   .1560287    -0.60   0.551    -.3995725    .2132709
   L0event_a |  -.4685157   .2801264    -1.67   0.095     -1.01865    .0816189
   L1event_a |  -.3914492    .302394    -1.29   0.196    -.9853146    .2024162
   L2event_a |   -.312196   .3697657    -0.84   0.399    -1.038371    .4139794
   L3event_a |  -.6372521   .3815773    -1.67   0.095    -1.386624    .1121198
   L4event_a |  -.1327344   .8970932    -0.15   0.882    -1.894518    1.629049
   L5event_a |   .5760312   1.408829     0.41   0.683    -2.190739    3.342802
   L6event_a |          0  (omitted)
   L7event_a |          0  (omitted)
   F7event_b |   .6073793   .3245413     1.87   0.062    -.0299807    1.244739
   F6event_b |   .3859813    .311364     1.24   0.216    -.2255002    .9974627
   F5event_b |  -.1468075   .2849704    -0.52   0.607    -.7064552    .4128402
   F4event_b |  -.1807463   .1916079    -0.94   0.346    -.5570411    .1955485
   F3event_b |   .0704248   .1710345     0.41   0.681    -.2654663    .4063159
   F2event_b |   .1366112   .1302256     1.05   0.295    -.1191362    .3923587
   L0event_b |    1.24061   .2428602     5.11   0.000     .7636621    1.717559
   L1event_b |   1.740599   .2754083     6.32   0.000      1.19973    2.281467
   L2event_b |   1.686442   .3280782     5.14   0.000     1.042135    2.330748
   L3event_b |   1.269908    .337481     3.76   0.000     .6071363    1.932681
   L4event_b |   2.680221   .9533024     2.81   0.005     .8080499    4.552393
   L5event_b |    2.61788   .9306508     2.81   0.005      .790194    4.445567
   L6event_b |          0  (omitted)
   L7event_b |          0  (omitted)
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
       _cons |   28.39323   .0422848   671.48   0.000     28.31018    28.47627
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------------------------+
                      Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------------------------+---------------------------------------|
                 wahl_id#stadtbez |       200           1         199     |
                           sb_new |       608         608           0    *|
                          wahl_id |         8           8           0     |
         wahl_id#c.ln_ew_ges_2013 |         8           0           8    ?|
          wahl_id#c.ew_biodt_2013 |         8           0           8    ?|
         wahl_id#c.ew_dtmihi_2013 |         8           0           8    ?|
          wahl_id#c.ew_ledig_2013 |         8           0           8    ?|
        wahl_id#c.ew_married_2013 |         8           0           8    ?|
     wahl_id#c.area_sb_ltw18_2013 |         8           0           8    ?|
         wahl_id#c.wb_anteil_2013 |         8           0           8    ?|
           wahl_id#c.wb_ausl_2013 |         8           8           0    ?|
          wahl_id#c.wb_18t24_2013 |         8           0           8    ?|
          wahl_id#c.wb_25t34_2013 |         8           0           8    ?|
          wahl_id#c.wb_35t44_2013 |         8           0           8    ?|
          wahl_id#c.wb_45t59_2013 |         8           0           8    ?|
           wahl_id#c.avg_dur_2013 |         8           0           8    ?|
           wahl_id#c.hh_kids_2013 |         8           0           8    ?|
 wahl_id#c.mpreis_flats_rent_2013 |         8           0           8    ?|
       wahl_id#c.street_dist_2013 |         8           0           8    ?|
--------------------------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 7 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =       3.11
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9830
                                                  Adj R-squared   =     0.9793
                                                  Within R-sq.    =     0.0165
Number of clusters (sb_new)  =        618         Root MSE        =     2.1520

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |  -.5194606   .4897617    -1.06   0.289    -1.481263    .4423415
   F6event_a |  -.3549603   .4461282    -0.80   0.427    -1.231074    .5211534
   F5event_a |  -1.159133   .5605807    -2.07   0.039     -2.26001   -.0582551
   F4event_a |  -.4608507    .245478    -1.88   0.061    -.9429244    .0212231
   F3event_a |  -.1760549   .2547986    -0.69   0.490    -.6764326    .3243227
   F2event_a |  -.9581535   .2709561    -3.54   0.000    -1.490261   -.4260456
   L0event_a |  -.0057945    .321193    -0.02   0.986    -.6365585    .6249696
   L1event_a |   .3179741   .3691848     0.86   0.389    -.4070371    1.042985
   L2event_a |   .3504615   .4289681     0.82   0.414    -.4919529    1.192876
   L3event_a |  -.5231147   .4364542    -1.20   0.231    -1.380231    .3340012
   L4event_a |  -.3364141    1.00338    -0.34   0.738    -2.306867    1.634039
   L5event_a |   1.220115    1.17889     1.03   0.301    -1.095008    3.535238
   L6event_a |  -.1733346   1.743043    -0.10   0.921     -3.59635    3.249681
   L7event_a |  -2.491241   .8527769    -2.92   0.004    -4.165938   -.8165441
   F7event_b |  -.0177713   .4207839    -0.04   0.966    -.8441136    .8085711
   F6event_b |   .3358514   .3575991     0.94   0.348    -.3664075     1.03811
   F5event_b |  -.3612439   .3501168    -1.03   0.303    -1.048809    .3263212
   F4event_b |  -.1347628   .2500102    -0.54   0.590     -.625737    .3562114
   F3event_b |   .1216133   .2223997     0.55   0.585    -.3151389    .5583655
   F2event_b |   .0665097   .2201314     0.30   0.763    -.3657879    .4988074
   L0event_b |  -.9270369   .2395729    -3.87   0.000    -1.397514   -.4565598
   L1event_b |  -.4294936   .3147575    -1.36   0.173     -1.04762    .1886323
   L2event_b |   .0103183   .3763415     0.03   0.978    -.7287472    .7493838
   L3event_b |  -.0259641   .4473599    -0.06   0.954    -.9044968    .8525685
   L4event_b |   1.361492   .8016802     1.70   0.090    -.2128607    2.935845
   L5event_b |   1.684185   1.124445     1.50   0.135    -.5240182    3.892388
   L6event_b |   .8178813   .7201283     1.14   0.257    -.5963184    2.232081
   L7event_b |   -.117627   1.342342    -0.09   0.930    -2.753739    2.518485
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
       _cons |   62.54161   .0524131  1193.24   0.000     62.43868    62.64454
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  32,    617) =      36.45
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9897
                                                  Adj R-squared   =     0.9874
                                                  Within R-sq.    =     0.4035
Number of clusters (sb_new)  =        618         Root MSE        =     1.6769

                               (Std. err. adjusted for 618 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |  -.3991504   .4971922    -0.80   0.422    -1.375545    .5772437
   F6event_a |  -.1285191   .3890575    -0.33   0.741    -.8925565    .6355183
   F5event_a |  -.7789067     .45844    -1.70   0.090    -1.679199    .1213853
   F4event_a |   -.330176   .2400858    -1.38   0.170    -.8016603    .1413084
   F3event_a |   -.005237   .2196265    -0.02   0.981    -.4365431    .4260692
   F2event_a |  -.2092364   .2001895    -1.05   0.296    -.6023717     .183899
   L0event_a |   .0677849   .2515921     0.27   0.788    -.4262958    .5618656
   L1event_a |    .261223   .2979176     0.88   0.381    -.3238324    .8462783
   L2event_a |   .5292166   .3176772     1.67   0.096     -.094643    1.153076
   L3event_a |   .1285293   .3219088     0.40   0.690    -.5036403     .760699
   L4event_a |   .0632299   1.104279     0.06   0.954     -2.10537     2.23183
   L5event_a |   1.633216   1.511177     1.08   0.280    -1.334458    4.600889
   L6event_a |  -.5034456   1.821156    -0.28   0.782    -4.079862    3.072971
   L7event_a |  -2.110179   .6062132    -3.48   0.001     -3.30067   -.9196878
   F7event_b |   .0388218   .3783092     0.10   0.918    -.7041079    .7817514
   F6event_b |   .4185302    .333577     1.25   0.210    -.2365537    1.073614
   F5event_b |  -.0300725   .2929975    -0.10   0.918    -.6054656    .5453207
   F4event_b |  -.0931106   .2016225    -0.46   0.644    -.4890602    .3028389
   F3event_b |   .0433047   .2039162     0.21   0.832    -.3571493    .4437587
   F2event_b |   .1135319    .161566     0.70   0.483    -.2037541    .4308178
   L0event_b |  -.7415667   .2052262    -3.61   0.000    -1.144593   -.3385401
   L1event_b |  -.2871223   .2632693    -1.09   0.276    -.8041347    .2298901
   L2event_b |   .0149148   .3007029     0.05   0.960    -.5756105      .60544
   L3event_b |    .168062   .3255244     0.52   0.606     -.471208    .8073321
   L4event_b |   1.224936   .5127709     2.39   0.017     .2179486    2.231924
   L5event_b |   1.437042   .6458679     2.22   0.026     .1686764    2.705408
   L6event_b |   .9340947   .5118643     1.82   0.069    -.0711127    1.939302
   L7event_b |   2.059719   .6130154     3.36   0.001     .8558692    3.263568
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
   ln_ew_ges |    1.71314   1.146201     1.49   0.136     -.537788    3.964068
   wb_anteil |  -.5719695   .0241901   -23.64   0.000    -.6194745   -.5244646
    ew_biodt |   .8391064   .0295626    28.38   0.000     .7810508    .8971619
   ew_dtmihi |  -.1534116    .052799    -2.91   0.004    -.2570991   -.0497241
       _cons |   38.76326   9.808621     3.95   0.000     19.50093    58.02559
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      33.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4440
Number of clusters (sb_new)  =        618         Root MSE        =     1.6210

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.4498347   .4925722    -0.91   0.361    -1.417156    .5174865
        F6event_a |  -.1719699   .3902489    -0.44   0.660     -.938347    .5944072
        F5event_a |   -.828655   .4505411    -1.84   0.066    -1.713435    .0561249
        F4event_a |  -.4298239   .2426357    -1.77   0.077    -.9063158     .046668
        F3event_a |  -.1075655   .2168072    -0.50   0.620    -.5333349     .318204
        F2event_a |  -.2497362   .2019212    -1.24   0.217    -.6462722    .1467999
        L0event_a |   .0158041    .239823     0.07   0.947    -.4551642    .4867724
        L1event_a |   .2082574   .2812297     0.74   0.459     -.344026    .7605409
        L2event_a |   .5381604   .3069533     1.75   0.080    -.0646395     1.14096
        L3event_a |   .1085883   .2874196     0.38   0.706     -.455851    .6730276
        L4event_a |   .1216059   .9220699     0.13   0.895     -1.68917    1.932382
        L5event_a |   1.926959   1.282873     1.50   0.134    -.5923686    4.446286
        L6event_a |   .1394028   1.303699     0.11   0.915    -2.420823    2.699628
        L7event_a |  -1.386941   .8623393    -1.61   0.108    -3.080417    .3065348
        F7event_b |   .1259241   .3695883     0.34   0.733    -.5998794    .8517276
        F6event_b |   .4698213   .3243514     1.45   0.148    -.1671453    1.106788
        F5event_b |   .0103831   .2850784     0.04   0.971    -.5494586    .5702248
        F4event_b |  -.0923843    .199051    -0.46   0.643    -.4832838    .2985153
        F3event_b |  -.0149294    .194926    -0.08   0.939    -.3977282    .3678695
        F2event_b |   .0600191   .1591538     0.38   0.706    -.2525298     .372568
        L0event_b |  -.6340596   .2007002    -3.16   0.002    -1.028198   -.2399212
        L1event_b |   -.144935    .252047    -0.58   0.565    -.6399089     .350039
        L2event_b |   .1314959    .278574     0.47   0.637    -.4155722    .6785641
        L3event_b |   .1609696   .3069884     0.52   0.600    -.4418993    .7638384
        L4event_b |   1.386832   .5405588     2.57   0.011     .3252744    2.448391
        L5event_b |   1.737936   .6427991     2.70   0.007     .4755971    3.000276
        L6event_b |   1.280575   .5287144     2.42   0.016     .2422772    2.318873
        L7event_b |   2.328298   .4952053     4.70   0.000     1.355806     3.30079
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.626521   1.066654     1.52   0.128    -.4681905    3.721233
         ew_biodt |   .7592254   .0315323    24.08   0.000     .6973017    .8211491
        ew_dtmihi |  -.1690904   .0518885    -3.26   0.001    -.2709899    -.067191
         ew_ledig |   .4207602   .0705442     5.96   0.000     .2822243    .5592962
       ew_married |   .6309884   .0690977     9.13   0.000     .4952933    .7666835
        wb_anteil |  -.5302829   .0240672   -22.03   0.000    -.5775465   -.4830192
          wb_ausl |  -.0529959   .0176265    -3.01   0.003     -.087611   -.0183807
         wb_18t24 |  -.0461903   .0262697    -1.76   0.079    -.0977791    .0053986
         wb_25t34 |  -.0185006   .0166385    -1.11   0.267    -.0511756    .0141745
         wb_35t44 |  -.0024399   .0210884    -0.12   0.908    -.0438536    .0389737
         wb_45t59 |  -.0240002    .019559    -1.23   0.220    -.0624104    .0144101
          avg_dur |   .0203595   .0222527     0.91   0.361    -.0233406    .0640597
          hh_kids |  -.1123654   .0358261    -3.14   0.002    -.1827212   -.0420096
mpreis_flats_rent |   .0115779   .0234248     0.49   0.621    -.0344241    .0575799
            _cons |   .4580862   9.951597     0.05   0.963    -19.08502     20.0012
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 16 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,609
Absorbing 3 HDFE groups                           F(  24,    607) =       2.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0013
                                                  R-squared       =     0.9886
                                                  Adj R-squared   =     0.9856
                                                  Within R-sq.    =     0.0165
Number of clusters (sb_new)  =        608         Root MSE        =     1.7947

                               (Std. err. adjusted for 608 clusters in sb_new)
------------------------------------------------------------------------------
             |               Robust
turnout_to~q | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   F7event_a |   .0344383   .4528443     0.08   0.939    -.8548935      .92377
   F6event_a |    .029978    .433716     0.07   0.945    -.8217881    .8817442
   F5event_a |   -.354396    .451871    -0.78   0.433    -1.241816    .5330243
   F4event_a |  -.1022218   .2425157    -0.42   0.674    -.5784936    .3740499
   F3event_a |  -.0384325   .2010731    -0.19   0.848    -.4333159     .356451
   F2event_a |  -.1658178   .1916298    -0.87   0.387    -.5421557    .2105201
   L0event_a |   .0153923   .2834996     0.05   0.957     -.541367    .5721515
   L1event_a |   .6315456   .3523657     1.79   0.074    -.0604582     1.32355
   L2event_a |   .5502852   .3893488     1.41   0.158    -.2143491     1.31492
   L3event_a |  -.4020854   .4226884    -0.95   0.342    -1.232195    .4280238
   L4event_a |  -.5245727   .6513718    -0.81   0.421    -1.803789    .7546432
   L5event_a |   1.025103   1.372978     0.75   0.456    -1.671262    3.721467
   L6event_a |          0  (omitted)
   L7event_a |          0  (omitted)
   F7event_b |   .2908447   .3964971     0.73   0.464    -.4878279    1.069517
   F6event_b |   .4239858   .3666329     1.16   0.248    -.2960371    1.144009
   F5event_b |   .0326215    .336257     0.10   0.923    -.6277469    .6929898
   F4event_b |  -.0049176   .1881764    -0.03   0.979    -.3744734    .3646382
   F3event_b |     .13076   .1841198     0.71   0.478    -.2308291    .4923491
   F2event_b |   .2830839   .1548551     1.83   0.068    -.0210329    .5872007
   L0event_b |  -.8654914   .2101298    -4.12   0.000    -1.278161   -.4528217
   L1event_b |  -.4950339   .2834819    -1.75   0.081    -1.051758    .0616904
   L2event_b |  -.0640387   .3366221    -0.19   0.849    -.7251242    .5970467
   L3event_b |   .3286077   .4131083     0.80   0.427    -.4826874    1.139903
   L4event_b |   1.015043    .872575     1.16   0.245    -.6985894    2.728675
   L5event_b |   1.033094   .8896384     1.16   0.246    -.7140487    2.780237
   L6event_b |          0  (omitted)
   L7event_b |          0  (omitted)
   F1event_a |          0  (omitted)
   F1event_b |          0  (omitted)
       _cons |   62.46235   .0447607  1395.47   0.000     62.37445    62.55026
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------------------------+
                      Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------------------------+---------------------------------------|
                 wahl_id#stadtbez |       200           1         199     |
                           sb_new |       608         608           0    *|
                          wahl_id |         8           8           0     |
         wahl_id#c.ln_ew_ges_2013 |         8           0           8    ?|
          wahl_id#c.ew_biodt_2013 |         8           0           8    ?|
         wahl_id#c.ew_dtmihi_2013 |         8           0           8    ?|
          wahl_id#c.ew_ledig_2013 |         8           0           8    ?|
        wahl_id#c.ew_married_2013 |         8           0           8    ?|
     wahl_id#c.area_sb_ltw18_2013 |         8           0           8    ?|
         wahl_id#c.wb_anteil_2013 |         8           0           8    ?|
           wahl_id#c.wb_ausl_2013 |         8           8           0    ?|
          wahl_id#c.wb_18t24_2013 |         8           0           8    ?|
          wahl_id#c.wb_25t34_2013 |         8           0           8    ?|
          wahl_id#c.wb_35t44_2013 |         8           0           8    ?|
          wahl_id#c.wb_45t59_2013 |         8           0           8    ?|
           wahl_id#c.avg_dur_2013 |         8           0           8    ?|
           wahl_id#c.hh_kids_2013 |         8           0           8    ?|
 wahl_id#c.mpreis_flats_rent_2013 |         8           0           8    ?|
       wahl_id#c.street_dist_2013 |         8           0           8    ?|
--------------------------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         
.         * TABLE C6. Robustness to Inclusion of Covariates–Effects by Distance
.         outreg using "$tables/Table_C6_ES_rob_covariates_dist2", replay tex replace fragment ///
>                 note("some:`somectr_1'; all: $ctr; interacted: $intctr")
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_C6
> _ES_rob_covariates_dist2.tex not found)
 -----------------------------------------------------------------------------------------------
                                             (1)       (2)              (3)             (4)    
                                                               Polling place turnout           
 -----------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.25     -0.20            -0.16            0.06   
                                            (0.25)    (0.25)          (0.24)           (0.25)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.37     -0.30            -0.25           -0.08   
                                            (0.24)    (0.24)          (0.24)           (0.21)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)   -0.56**    -0.19            -0.20           -0.07   
                                            (0.21)    (0.18)          (0.18)           (0.18)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.42      0.46            0.48             0.48   
                                            (0.36)    (0.35)          (0.34)           (0.33)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.65      0.61            0.60            1.02**  
                                            (0.34)    (0.33)          (0.31)           (0.35)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.54      0.57            0.49            0.86*   
                                            (0.38)    (0.35)          (0.35)           (0.39)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.06      0.08            0.10             0.18   
                                            (0.22)    (0.22)          (0.21)           (0.21)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.08      0.05            0.05             0.06   
                                            (0.22)    (0.21)          (0.20)           (0.21)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.15      0.18            0.15             0.15   
                                            (0.18)    (0.15)          (0.15)           (0.15)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.05***  -1.97***        -1.89***         -2.11*** 
                                            (0.28)    (0.27)          (0.27)           (0.26)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.15***  -2.11***        -1.96***         -2.24*** 
                                            (0.28)    (0.27)          (0.27)           (0.27)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.63***  -1.68***        -1.59***         -1.75*** 
                                            (0.33)    (0.32)          (0.31)           (0.33)  
  R2                                         0.97      0.97            0.97             0.97   
  N                                         4,666     4,666            4,666           4,609   
  Time-varying controls                      none      some             all            none    
  pre-treatment covariates x election FE                                                yes    
 -----------------------------------------------------------------------------------------------
some:ln_ew_ges wb_anteil ew_biodt ew_dtmihi ; all: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_marrie
> d wb_anteil                                  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 avg_dur
>  hh_kids mpreis_flats_rent; interacted: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_married area_sb
> _ltw18                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 a
> vg_dur hh_kids mpreis_flats_rent                                        street_dist


 ------------------------------------------------------------------------------------------------
                                             (5)      (6)          (7)          (8)      (9)    
                                                             Mail-in turnout                    
 ------------------------------------------------------------------------------------------------
  (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.21    -0.13        -0.27        -0.17    -0.46   
                                           (0.22)   (0.22)       (0.22)       (0.20)    (0.25)  
  (N-)x\hspace{.7cm}Reassignment (#t-3#)    0.19     0.29         0.14         0.05     -0.18   
                                           (0.24)   (0.22)       (0.22)       (0.19)    (0.25)  
  (N-)x\hspace{.7cm}Reassignment (#t-2#)   -0.40*    -0.02        -0.05        -0.09   -0.96*** 
                                           (0.19)   (0.18)       (0.18)       (0.16)    (0.27)  
  (N-)x\hspace{.7cm}Reassignment (#t+0#)    -0.42    -0.39        -0.46        -0.47    -0.01   
                                           (0.33)   (0.31)       (0.31)       (0.28)    (0.32)  
  (N-)x\hspace{.7cm}Reassignment (#t+1#)    -0.34    -0.35        -0.39        -0.39     0.32   
                                           (0.33)   (0.31)       (0.31)       (0.30)    (0.37)  
  (N-)x\hspace{.7cm}Reassignment (#t+2#)    -0.19    -0.04        0.05         -0.31     0.35   
                                           (0.39)   (0.36)       (0.36)       (0.37)    (0.43)  
  (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.19    -0.17        -0.19        -0.18    -0.13   
                                           (0.22)   (0.19)       (0.19)       (0.19)    (0.25)  
  (N+)x\hspace{.7cm}Reassignment (#t-3#)    0.04     -0.01        -0.07        0.07      0.12   
                                           (0.19)   (0.18)       (0.18)       (0.17)    (0.22)  
  (N+)x\hspace{.7cm}Reassignment (#t-2#)    -0.08    -0.06        -0.09        0.14      0.07   
                                           (0.15)   (0.15)       (0.15)       (0.13)    (0.22)  
  (N+)x\hspace{.7cm}Reassignment (#t+0#)   1.12***  1.23***      1.26***      1.24***  -0.93*** 
                                           (0.27)   (0.26)       (0.26)       (0.24)    (0.24)  
  (N+)x\hspace{.7cm}Reassignment (#t+1#)   1.72***  1.82***      1.82***      1.74***   -0.43   
                                           (0.29)   (0.27)       (0.27)       (0.28)    (0.31)  
  (N+)x\hspace{.7cm}Reassignment (#t+2#)   1.64***  1.69***      1.72***      1.69***    0.01   
                                           (0.37)   (0.33)       (0.33)       (0.33)    (0.38)  
  R2                                        0.95     0.96         0.96         0.97      0.98   
  N                                         4,666    4,666        4,666        4,609    4,666   
  Time-varying controls                     none     some          all         none      none   
  pre-treatment covariates x election FE                                        yes             
 ------------------------------------------------------------------------------------------------
some:ln_ew_ges wb_anteil ew_biodt ew_dtmihi ; all: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_marrie
> d wb_anteil                                  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 avg_dur
>  hh_kids mpreis_flats_rent; interacted: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_married area_sb
> _ltw18                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 a
> vg_dur hh_kids mpreis_flats_rent                                        street_dist


          -----------------------------------------------------------------------------
                                                      (10)        (11)         (12)   
                                                              Total turnout           
          -----------------------------------------------------------------------------
           (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.33        -0.43       -0.10   
                                                     (0.24)      (0.24)       (0.24)  
           (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.01        -0.11       -0.04   
                                                     (0.22)      (0.22)       (0.20)  
           (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.21        -0.25       -0.17   
                                                     (0.20)      (0.20)       (0.19)  
           (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.07        0.02         0.02   
                                                     (0.25)      (0.24)       (0.28)  
           (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.26        0.21         0.63   
                                                     (0.30)      (0.28)       (0.35)  
           (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.53        0.54         0.55   
                                                     (0.32)      (0.31)       (0.39)  
           (N+)x\hspace{.7cm}Reassignment (#t-4#)    -0.09        -0.09       -0.00   
                                                     (0.20)      (0.20)       (0.19)  
           (N+)x\hspace{.7cm}Reassignment (#t-3#)     0.04        -0.01        0.13   
                                                     (0.20)      (0.19)       (0.18)  
           (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.11        0.06         0.28   
                                                     (0.16)      (0.16)       (0.15)  
           (N+)x\hspace{.7cm}Reassignment (#t+0#)   -0.74***     -0.63**     -0.87*** 
                                                     (0.21)      (0.20)       (0.21)  
           (N+)x\hspace{.7cm}Reassignment (#t+1#)    -0.29        -0.14       -0.50   
                                                     (0.26)      (0.25)       (0.28)  
           (N+)x\hspace{.7cm}Reassignment (#t+2#)     0.01        0.13        -0.06   
                                                     (0.30)      (0.28)       (0.34)  
           R2                                         0.99        0.99         0.99   
           N                                         4,666        4,666       4,609   
           Time-varying controls                      some         all        none    
           pre-treatment covariates x election FE                              yes    
          -----------------------------------------------------------------------------
some:ln_ew_ges wb_anteil ew_biodt ew_dtmihi ; all: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_marrie
> d wb_anteil                                  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 avg_dur
>  hh_kids mpreis_flats_rent; interacted: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_married area_sb
> _ltw18                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t59 a
> vg_dur hh_kids mpreis_flats_rent                                        street_dist


.         cleantex     "$tables/Table_C6_ES_rob_covariates_dist2.tex"  ,  replace

\begin{tabular}{lcccccccccccc}
\toprule  & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12)\\
 &  &  & Polling place turnout &  &  &  & Mail-in turnout &  &  &  & Total turnout & \\
 (N-)x\hspace{.7cm}Reassignment ($ t-4$) & -0.25 & -0.20 & -0.16 & 0.06 & -0.21 & -0.13 & -0.27 & 
> -0.17 & -0.46 & -0.33 & -0.43 & -0.10\\
 & (0.25) & (0.25) & (0.24) & (0.25) & (0.22) & (0.22) & (0.22) & (0.20) & (0.25) & (0.24) & (0.24
> ) & (0.24)\\
(N-)x\hspace{.7cm}Reassignment ($ t-3$) & -0.37 & -0.30 & -0.25 & -0.08 & 0.19 & 0.29 & 0.14 & 0.0
> 5 & -0.18 & -0.01 & -0.11 & -0.04\\
 & (0.24) & (0.24) & (0.24) & (0.21) & (0.24) & (0.22) & (0.22) & (0.19) & (0.25) & (0.22) & (0.22
> ) & (0.20)\\
(N-)x\hspace{.7cm}Reassignment ($ t-2$) & -0.56** & -0.19 & -0.20 & -0.07 & -0.40* & -0.02 & -0.05
>  & -0.09 & -0.96*** & -0.21 & -0.25 & -0.17\\
 & (0.21) & (0.18) & (0.18) & (0.18) & (0.19) & (0.18) & (0.18) & (0.16) & (0.27) & (0.20) & (0.20
> ) & (0.19)\\
(N-)x\hspace{.7cm}Reassignment ($ t+0$) & 0.42 & 0.46 & 0.48 & 0.48 & -0.42 & -0.39 & -0.46 & -0.4
> 7 & -0.01 & 0.07 & 0.02 & 0.02\\
 & (0.36) & (0.35) & (0.34) & (0.33) & (0.33) & (0.31) & (0.31) & (0.28) & (0.32) & (0.25) & (0.24
> ) & (0.28)\\
(N-)x\hspace{.7cm}Reassignment ($ t+1$) & 0.65 & 0.61 & 0.60 & 1.02** & -0.34 & -0.35 & -0.39 & -0
> .39 & 0.32 & 0.26 & 0.21 & 0.63\\
 & (0.34) & (0.33) & (0.31) & (0.35) & (0.33) & (0.31) & (0.31) & (0.30) & (0.37) & (0.30) & (0.28
> ) & (0.35)\\
(N-)x\hspace{.7cm}Reassignment ($ t+2$) & 0.54 & 0.57 & 0.49 & 0.86* & -0.19 & -0.04 & 0.05 & -0.3
> 1 & 0.35 & 0.53 & 0.54 & 0.55\\
 & (0.38) & (0.35) & (0.35) & (0.39) & (0.39) & (0.36) & (0.36) & (0.37) & (0.43) & (0.32) & (0.31
> ) & (0.39)\\
(N+)x\hspace{.7cm}Reassignment ($ t-4$) & 0.06 & 0.08 & 0.10 & 0.18 & -0.19 & -0.17 & -0.19 & -0.1
> 8 & -0.13 & -0.09 & -0.09 & -0.00\\
 & (0.22) & (0.22) & (0.21) & (0.21) & (0.22) & (0.19) & (0.19) & (0.19) & (0.25) & (0.20) & (0.20
> ) & (0.19)\\
(N+)x\hspace{.7cm}Reassignment ($ t-3$) & 0.08 & 0.05 & 0.05 & 0.06 & 0.04 & -0.01 & -0.07 & 0.07 
> & 0.12 & 0.04 & -0.01 & 0.13\\
 & (0.22) & (0.21) & (0.20) & (0.21) & (0.19) & (0.18) & (0.18) & (0.17) & (0.22) & (0.20) & (0.19
> ) & (0.18)\\
(N+)x\hspace{.7cm}Reassignment ($ t-2$) & 0.15 & 0.18 & 0.15 & 0.15 & -0.08 & -0.06 & -0.09 & 0.14
>  & 0.07 & 0.11 & 0.06 & 0.28\\
 & (0.18) & (0.15) & (0.15) & (0.15) & (0.15) & (0.15) & (0.15) & (0.13) & (0.22) & (0.16) & (0.16
> ) & (0.15)\\
(N+)x\hspace{.7cm}Reassignment ($ t+0$) & -2.05*** & -1.97*** & -1.89*** & -2.11*** & 1.12*** & 1.
> 23*** & 1.26*** & 1.24*** & -0.93*** & -0.74*** & -0.63** & -0.87***\\
 & (0.28) & (0.27) & (0.27) & (0.26) & (0.27) & (0.26) & (0.26) & (0.24) & (0.24) & (0.21) & (0.20
> ) & (0.21)\\
(N+)x\hspace{.7cm}Reassignment ($ t+1$) & -2.15*** & -2.11*** & -1.96*** & -2.24*** & 1.72*** & 1.
> 82*** & 1.82*** & 1.74*** & -0.43 & -0.29 & -0.14 & -0.50\\
 & (0.28) & (0.27) & (0.27) & (0.27) & (0.29) & (0.27) & (0.27) & (0.28) & (0.31) & (0.26) & (0.25
> ) & (0.28)\\
(N+)x\hspace{.7cm}Reassignment ($ t+2$) & -1.63*** & -1.68*** & -1.59*** & -1.75*** & 1.64*** & 1.
> 69*** & 1.72*** & 1.69*** & 0.01 & 0.01 & 0.13 & -0.06\\
 & (0.33) & (0.32) & (0.31) & (0.33) & (0.37) & (0.33) & (0.33) & (0.33) & (0.38) & (0.30) & (0.28
> ) & (0.34)\\
$ R^2$  & 0.97 & 0.97 & 0.97 & 0.97 & 0.95 & 0.96 & 0.96 & 0.97 & 0.98 & 0.99 & 0.99 & 0.99\\
Observations & 4,666 & 4,666 & 4,666 & 4,609 & 4,666 & 4,666 & 4,666 & 4,609 & 4,666 & 4,666 & 4,6
> 66 & 4,609\\
Time-varying controls & none & some & all & none  & none & some & all & none  & none & some & all 
> & none \\
pre-treatment covariates x election FE &  &  &  & yes &  &  &  & yes &  &  &  & yes\\
\bottomrule\end{tabular}
\smallskipsome:ln_ew_ges wb_anteil ew_biodt ew_dtmihi ; all: ln_ew_ges ew_biodt ew_dtmihi ew_ledig
>  ew_married wb_anteil                                  wb_ausl wb_18t24 wb_25t34 wb_35t44 wb_45t
> 59 avg_dur hh_kids mpreis_flats_rent; interacted: ln_ew_ges ew_biodt ew_dtmihi ew_ledig ew_marri
> ed area_sb_ltw18                                  wb_anteil  wb_ausl wb_18t24 wb_25t34 wb_35t44 
> wb_45t59 avg_dur hh_kids mpreis_flats_rent                                        street_dist\\
\smallskip


.         
. 
end of do-file
Running: 04i_rob_alt_treatm_figures_c8_c9_c10_c11_table_c7.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output: Figure C.8, C.9, C.10, C.11, Table C.7
> 
> Tasks: Robustness to different estimators and continuout treatments 
>         
>         
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

.         
.         
. ********************************************************************************
.                 // Prep Estimation //
. ********************************************************************************
. 
.         // compute group ids for DISTANCE increase/decrease, 0 else in K
.         //      > first election w 100% of addresses reassigned
.         gen     tmp = (del_street_dist>0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase in event (Ei), 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease in event (Ei), 0 else" 

.         
.         // compute group ids for DISTANCE increase/decrease, 0 else in K50
.         //      > first election w 50%+ reass.
.         cap drop tmp*

.         gen     tmp = (del_street_dist>0)                       if K50==0
(4,572 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up50 = tmp[1]
(1,968 missing values generated)

.         replace ind_dist_up50 = 0                                       if missing(Ei50)
(1,968 real changes made)

.         lab var ind_dist_up50 "=1 if dist increase in event (Ei50), 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K50==0
(4,572 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn50 = tmp[1]
(1,968 missing values generated)

.         replace ind_dist_dn50 = 0                                       if missing(Ei50)
(1,968 real changes made)

.         lab var ind_dist_dn50 "=1 if dist decrease in event (Ei50), 0 else"             

. 
.         // compute id for DISTANCE increase/decrease, 0 else in K_max 
.         //      > election with highest share of reassignments
.         cap drop tmp*

.         gen     tmp = (del_street_dist>0)                       if K_max==0
(4,431 missing values generated)

.         bys sb_new (tmp): gen ind_dist_upX = tmp[1]
(840 missing values generated)

.         replace ind_dist_upX = 0                                        if missing(Ei_max)
(840 real changes made)

.         lab var ind_dist_upX "=1 if dist increase in event, 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K_max==0
(4,431 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dnX = tmp[1]
(840 missing values generated)

.         replace ind_dist_dnX = 0                                        if missing(Ei_max)
(840 real changes made)

.         lab var ind_dist_dnX "=1 if dist decrease in event, 0 else"     

.         
.         // gen del_street_dist_max := ABSOLUTE value of delta dist in K_max=0;
.         // gen del_street_dist_Kmax:= actual value in K_max=0
.         //              -> cont. across time.
.         gen del_street_dist_max=del_street_dist if K_max==0
(4,431 missing values generated)

.         grconst del_street_dist_max, by(sb_new) fill
Note: there are by-groups where *del_street_dist_max* is entirely missing.
(3591 real changes made)

.         replace del_street_dist_max=0 if Ei_max ==.
(840 real changes made)

.         
.         gen     del_street_dist_Kmax = del_street_dist_max

.         replace del_street_dist_max = del_street_dist_max*(-1) if del_street_dist_max<0
(1,600 real changes made)

.                 
.         //  del_street_dist_const := change in K==0
.                 gen del_street_dist_const = del_street_dist if K==0
(4,664 missing values generated)

.                 bys sb_new (del_street_dist_const): replace del_street_dist_const=del_street_dis
> t_const[1]      //time constant 
(1960 real changes made)

.                 replace del_street_dist_const=0 if missing(K)   
(2,704 real changes made)

.                 lab var del_street_dist_const "change in street dist in K=0, NT=0"      

.         
.         // del_street_dist_const_abs := ABSOLUTE value in K=0
.                 gen del_street_dist_const_abs = abs(del_street_dist_const)

.                 lab var del_street_dist_const_abs "abs(change in street dist in K=0), NT=0"     
>         

.         
. * gen leads and lags
.         
.  * TWFE OLS; Event = 100% reassg.
.         // gen leads and lags
.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         order F1event, last

.         
.  * TWFE OLS; Event = 50%+ reassg.
.         // gen leads and lags
.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event50 = K50==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event50 = K50==`l'
  3.         }

.         order F1event50, last   

.         
.         
. * Rescale ES Dummies; Event = Election where STRONGEST reassgn. happend.        
.         // gen leads and lags: Event = LARGEST  reassg.
.         forvalues l = 7(-1)1 {
  2.                 gen F`l'eventX = K_max==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'eventX = K_max==`l'
  3.         }

.         order F1eventX, last    

.         
.         // rescale by treatment intensity
.         gen treat_simple_max = treat_simple if K_max==0 
(4,431 missing values generated)

.         grconst treat_simple_max, by(sb_new) fill 
Note: there are by-groups where *treat_simple_max* is entirely missing.
(3591 real changes made)

.         replace treat_simple_max=0 if Ei_max==.
(840 real changes made)

.         
.         forvalues l = 7(-1)1 {
  2.                 gen F`l'eventRS = F`l'eventX
  3.                 replace F`l'eventRS=F`l'eventRS*treat_simple_max
  4.         }       
(98 real changes made)
(99 real changes made)
(100 real changes made)
(218 real changes made)
(218 real changes made)
(219 real changes made)
(219 real changes made)

.         forvalues l = 0/7 {
  2.                 gen L`l'eventRS = L`l'eventX
  3.                 replace L`l'eventRS= L`l'eventRS*treat_simple_max
  4.         }
(233 real changes made)
(135 real changes made)
(134 real changes made)
(133 real changes made)
(15 real changes made)
(15 real changes made)
(14 real changes made)
(14 real changes made)

.         order F1eventRS, last

.         
.         cap drop tmp*

.  * Prep for estimation with did_multiplegt of de Chaisemartin and D'Haultfoeuille (2020)
.         
.         // treat_simple_clean := treat_simple in election with HIGHEST reassng. shock (% reass);
>  zero before;
.         cap drop treat_simple_clean

.         gen     treat_simple_clean = 0 if K_max!=0
(513 missing values generated)

.         bys sb_new: ereplace treat_simple_clean = max(treat_simple) if K_max>=0 & !missing(Ei_ma
> x)
(3,250 missing values generated)
(3250 missing values generated)
(1,694 real changes made)

.         
.         assert treat_simple_clean==0 if Ei_max==.       

.         
.         // gen del_street_dist_clean:= 0 pre-treatment, ABS(del_street_dist) posttreatment
.         gen del_street_dist_clean = 0 if K_max!=0
(513 missing values generated)

.         bys sb_new: replace del_street_dist_clean = del_street_dist_max if K_max>=0 & !missing(E
> i_max)
(1694 real changes made)

.         assert del_street_dist_clean==0 if Ei_max==.    

.         lab var del_street_dist_clean "0 preT, abs(Δdist) postT, K_max"

.         
.         // gen del_street_dist_clean2:= 0 pre-treatment, del_street_dist (pos/neg) posttreatment
.         gen     del_street_dist_clean2=del_street_dist_Kmax

.         replace del_street_dist_clean2=0 if K_max<0
(2,410 real changes made)

.         lab var del_street_dist_clean2 "0 preT, Δdist postT, K_max"

.         
.  * Rolling treatment: treatment dose (change in dist) as of last election with reassignment     
.         // del_street_dist_roll := Δdist as of last election with reass. 
.         cap drop del_street_dist_roll

.         gen del_street_dist_roll = del_street_dist

.         *bys sb_new (wahl_id): replace del_street_dist_roll = del_street_dist_roll[_n-1] if abs(
> del_street_dist_roll[_n-1]) > abs(del_street_dist_roll[_n]) & del_street_dist_roll[_n-1]!=.     
.         bys sb_new (wahl_id): replace del_street_dist_roll = del_street_dist_roll[_n-1] if del_s
> treet_dist_roll[_n-1] !=0 & del_street_dist_roll[_n-1]!=. & del_street_dist_roll[_n] ==0        
(1091 real changes made)

.         lab var del_street_dist_roll "Δdist as of last election with reassgn."

.         
.         // del_street_dist_roll := Δdist as of last election with reass. 
.         cap drop del_street_dist_cum

.         gen del_street_dist_cum = del_street_dist

.         bys sb_new (wahl_id): replace del_street_dist_cum = del_street_dist_cum[_n-1]+ del_stree
> t_dist_cum if del_street_dist_cum[_n-1] !=.     
(1586 real changes made)

.         
.         // election FE x treatment in first election: ctf* 
.         cap drop D1

.         bys sb_new (wahl_id): gen D1=treat_simple_clean[1]

.         
.         cap drop ctf* 

.         tab1 wahl_id, gen(ctf)

-> tabulation of wahl_id  

Election ID |
      (num, |
chronologic |
        al) |      Freq.     Percent        Cum.
------------+-----------------------------------
      LTW13 |        618       12.50       12.50
      BTW13 |        618       12.50       25.00
      KOW14 |        618       12.50       37.50
      EUW14 |        618       12.50       50.00
      BTW17 |        618       12.50       62.50
      LTW18 |        618       12.50       75.00
      EUW19 |        618       12.50       87.50
      KOW20 |        618       12.50      100.00
------------+-----------------------------------
      Total |      4,944      100.00

.         foreach v of varlist ctf* {
  2.                 replace `v' = `v' * D1
  3.         }
variable ctf1 was byte now float
(608 real changes made)
variable ctf2 was byte now float
(608 real changes made)
variable ctf3 was byte now float
(608 real changes made)
variable ctf4 was byte now float
(608 real changes made)
variable ctf5 was byte now float
(608 real changes made)
variable ctf6 was byte now float
(608 real changes made)
variable ctf7 was byte now float
(608 real changes made)
variable ctf8 was byte now float
(608 real changes made)

.         // polynomial_ ctf_2*
.         tab1 wahl_id, gen(ctf_2)

-> tabulation of wahl_id  

Election ID |
      (num, |
chronologic |
        al) |      Freq.     Percent        Cum.
------------+-----------------------------------
      LTW13 |        618       12.50       12.50
      BTW13 |        618       12.50       25.00
      KOW14 |        618       12.50       37.50
      EUW14 |        618       12.50       50.00
      BTW17 |        618       12.50       62.50
      LTW18 |        618       12.50       75.00
      EUW19 |        618       12.50       87.50
      KOW20 |        618       12.50      100.00
------------+-----------------------------------
      Total |      4,944      100.00

.         foreach v of varlist ctf_2* {
  2.                 replace `v' = `v' * D1^2
  3.         }
variable ctf_21 was byte now float
(608 real changes made)
variable ctf_22 was byte now float
(608 real changes made)
variable ctf_23 was byte now float
(608 real changes made)
variable ctf_24 was byte now float
(608 real changes made)
variable ctf_25 was byte now float
(608 real changes made)
variable ctf_26 was byte now float
(608 real changes made)
variable ctf_27 was byte now float
(608 real changes made)
variable ctf_28 was byte now float
(608 real changes made)

. 
.         // election FE x treatment in first election: ctf* 
.         cap drop D1

.         bys sb_new (wahl_id): gen D1=del_street_dist_roll[1]

.         
.         cap drop dctf* 

.         tab1 wahl_id, gen(dctf)

-> tabulation of wahl_id  

Election ID |
      (num, |
chronologic |
        al) |      Freq.     Percent        Cum.
------------+-----------------------------------
      LTW13 |        618       12.50       12.50
      BTW13 |        618       12.50       25.00
      KOW14 |        618       12.50       37.50
      EUW14 |        618       12.50       50.00
      BTW17 |        618       12.50       62.50
      LTW18 |        618       12.50       75.00
      EUW19 |        618       12.50       87.50
      KOW20 |        618       12.50      100.00
------------+-----------------------------------
      Total |      4,944      100.00

.         foreach v of varlist dctf* {
  2.                 replace `v' = `v' * D1
  3.         }
variable dctf1 was byte now float
(618 real changes made)
variable dctf2 was byte now float
(618 real changes made)
variable dctf3 was byte now float
(618 real changes made)
variable dctf4 was byte now float
(618 real changes made)
variable dctf5 was byte now float
(618 real changes made)
variable dctf6 was byte now float
(618 real changes made)
variable dctf7 was byte now float
(618 real changes made)
variable dctf8 was byte now float
(618 real changes made)

. 
.         
. *** TABLE C.7: Sample Composition by Treatment Definition
. cap frame drop tablec7

.         frame create tablec7 str60 tdef int treated int untreated       

. 
.   * binary treatment, 100% addresses reassigned
.         distinct sb_new if Ei!=.        //      treated: 280

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2240        280

.         local unt = r(ndistinct)

.         distinct sb_new if Ei==.        //      untreated:   338

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

.         frame post tablec7 ("binary treatment, 100% reassigned") (`unt') (`r(ndistinct)')       

.   * binary treatment, 50%+ addresses reassigned
.         distinct sb_new if Ei50!=.      //      treated: 372

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2976        372

.         local unt = r(ndistinct)

.         distinct sb_new if Ei50==.      //      untreated:   246

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       1968        246

.         frame post tablec7 ("binary treatment, 50%+ reassigned") (`unt') (`r(ndistinct)')

.   * continuous treatment, (% addresses reassigned or Δdistance)
.         distinct sb_new if Ei_max!=.    //      treated: 513

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       4104        513

.         local unt = r(ndistinct)

.         distinct sb_new if Ei_max==.    //      untreated:   105

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        840        105

.         frame post tablec7 ("continuous treatment, (% addresses reassigned or Δdistance)") (`unt
> ') (`r(ndistinct)')    

.         frame tablec7: list,  table notrim

                                                              tdef   treated   untrea~d  
  1.                             binary treatment, 100% reassigned       280        338  
  2.                             binary treatment, 50%+ reassigned       372        246  
  3.   continuous treatment, (% addresses reassigned or Δdistance)       513        105  

.         frame tablec7: texsave using "$tables/Table_C7_Smpl_comp_by_treat_def.tex", ///
>                                                 align(lcc) hlines(1 -1) frag replace noendash

.                                                 
. ********************************************************************************
. // Binary T (100%), binary T (50%+), cont. T (TWFE), cont. T (dC et al, 2023) //
. // -> Pooled Reassignments (Figure C8) 
. ********************************************************************************                
. 
.         // Estimate baseline ES
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                   reghdfe `v' F7event-L7event F1event $ctr $wgt  if smpl_trim==1, absorb(i.wa
> hl_id#i.stadtbez i.sb_new) cluster(sb_new)
  3.                 
.                 estimates store `v'_bsl
  4.         }
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      17.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9720
                                                  Adj R-squared   =     0.9658
                                                  Within R-sq.    =     0.1691
Number of clusters (sb_new)  =        618         Root MSE        =     1.6979

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1660466   .3609916    -0.46   0.646    -.8749677    .5428746
          F6event |   .0365542   .3229698     0.11   0.910    -.5976991    .6708075
          F5event |   .2427259   .2553086     0.95   0.342    -.2586533    .7441051
          F4event |   .0132213   .1747424     0.08   0.940    -.3299406    .3563833
          F3event |  -.0565081    .169943    -0.33   0.740    -.3902449    .2772287
          F2event |    .006289   .1214372     0.05   0.959    -.2321913    .2447694
          L0event |  -.9985596   .2347416    -4.25   0.000    -1.459549   -.5375702
          L1event |   -.892731   .2344548    -3.81   0.000    -1.353157   -.4323048
          L2event |  -.7515249   .2578216    -2.91   0.004    -1.257839   -.2452106
          L3event |  -.2931705   .2663112    -1.10   0.271    -.8161567    .2298157
          L4event |  -.8782313   .4731179    -1.86   0.064    -1.807348    .0508853
          L5event |  -.5487131   .6172865    -0.89   0.374     -1.76095    .6635241
          L6event |    1.07717   .7208125     1.49   0.136    -.3383731    2.492714
          L7event |   .9427971   1.187553     0.79   0.428    -1.389339    3.274933
          F1event |          0  (omitted)
        ln_ew_ges |  -.7878053   .9548651    -0.83   0.410    -2.662985    1.087374
         ew_biodt |    .373603   .0281093    13.29   0.000     .3184015    .4288045
        ew_dtmihi |   .0630129   .0513196     1.23   0.220    -.0377693    .1637951
         ew_ledig |   .2127399   .0574696     3.70   0.000     .0998801    .3255996
       ew_married |   .4312672   .0590852     7.30   0.000     .3152348    .5472997
        wb_anteil |  -.2895078   .0204524   -14.16   0.000    -.3296726   -.2493431
          wb_ausl |   .0153767   .0162706     0.95   0.345    -.0165758    .0473293
         wb_18t24 |  -.0164981   .0295896    -0.56   0.577    -.0746067    .0416105
         wb_25t34 |  -.0724461   .0193717    -3.74   0.000    -.1104886   -.0344037
         wb_35t44 |    .006101   .0230068     0.27   0.791    -.0390802    .0512822
         wb_45t59 |   .0140084   .0220328     0.64   0.525      -.02926    .0572768
          avg_dur |  -.0288813   .0210954    -1.37   0.171    -.0703088    .0125462
          hh_kids |  -.0506236   .0408723    -1.24   0.216    -.1308893    .0296421
mpreis_flats_rent |   .0303932   .0255105     1.19   0.234    -.0197047    .0804911
            _cons |   11.36038   9.043266     1.26   0.210    -6.398933    29.11969
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      16.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9616
                                                  Adj R-squared   =     0.9531
                                                  Within R-sq.    =     0.2040
Number of clusters (sb_new)  =        618         Root MSE        =     1.6847

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |    .083637   .3314703     0.25   0.801    -.5673098    .7345839
          F6event |   .2184141   .2600152     0.84   0.401    -.2922081    .7290362
          F5event |  -.4796951   .2637694    -1.82   0.069    -.9976898    .0382995
          F4event |  -.2325114   .1614186    -1.44   0.150    -.5495078     .084485
          F3event |   .0074853   .1524588     0.05   0.961    -.2919157    .3068862
          F2event |   -.059996   .1248273    -0.48   0.631    -.3051339    .1851419
          L0event |   .6092887   .2189284     2.78   0.006     .1793535    1.039224
          L1event |   .9038027   .2273231     3.98   0.000      .457382    1.350223
          L2event |   1.047491   .2631015     3.98   0.000     .5308081    1.564174
          L3event |   .4175165   .2577372     1.62   0.106    -.0886321    .9236651
          L4event |   1.531036   .6502778     2.35   0.019     .2540104    2.808063
          L5event |   2.356691   .5459981     4.32   0.000     1.284451    3.428931
          L6event |  -.3848372    .877942    -0.44   0.661    -2.108954     1.33928
          L7event |   -.503072    .765562    -0.66   0.511    -2.006495    1.000351
          F1event |          0  (omitted)
        ln_ew_ges |   2.465914   1.349016     1.83   0.068    -.1833058    5.115133
         ew_biodt |    .388145   .0296323    13.10   0.000     .3299526    .4463375
        ew_dtmihi |  -.2303872   .0595829    -3.87   0.000     -.347397   -.1133774
         ew_ledig |   .2111155   .0802108     2.63   0.009     .0535962    .3686348
       ew_married |   .2074512   .0799261     2.60   0.010     .0504911    .3644114
        wb_anteil |  -.2425344   .0223631   -10.85   0.000    -.2864513   -.1986174
          wb_ausl |   -.068913   .0145836    -4.73   0.000    -.0975526   -.0402734
         wb_18t24 |  -.0273058   .0275422    -0.99   0.322    -.0813937    .0267821
         wb_25t34 |   .0526598   .0194716     2.70   0.007     .0144212    .0908985
         wb_35t44 |  -.0089296   .0249211    -0.36   0.720    -.0578702    .0400109
         wb_45t59 |   -.038132   .0205362    -1.86   0.064    -.0784614    .0021973
          avg_dur |   .0459808   .0233899     1.97   0.050     .0000472    .0919144
          hh_kids |  -.0642629   .0417916    -1.54   0.125    -.1463339    .0178081
mpreis_flats_rent |  -.0155286   .0234069    -0.66   0.507    -.0614954    .0304383
            _cons |  -11.72708   11.35784    -1.03   0.302     -34.0318    10.57764
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  28,    617) =      44.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4395
Number of clusters (sb_new)  =        618         Root MSE        =     1.6245

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.0824097   .3259399    -0.25   0.800    -.7224958    .5576764
          F6event |   .2549675   .2761707     0.92   0.356    -.2873811    .7973161
          F5event |  -.2369689   .2619342    -0.90   0.366    -.7513594    .2774217
          F4event |  -.2192898   .1694872    -1.29   0.196    -.5521315     .113552
          F3event |  -.0490231   .1579758    -0.31   0.756    -.3592586    .2612124
          F2event |  -.0537065   .1322374    -0.41   0.685    -.3133964    .2059834
          L0event |  -.3892706   .1633916    -2.38   0.018    -.7101417   -.0683996
          L1event |    .011072    .200952     0.06   0.956    -.3835608    .4057048
          L2event |   .2959665    .224252     1.32   0.187    -.1444233    .7363563
          L3event |   .1243464   .2332215     0.53   0.594    -.3336578    .5823505
          L4event |   .6528057   .6207241     1.05   0.293    -.5661824    1.871794
          L5event |   1.807978   .7132727     2.53   0.011      .407241    3.208714
          L6event |   .6923315   .7952045     0.87   0.384     -.869304    2.253967
          L7event |   .4397263   1.099927     0.40   0.689    -1.720328    2.599781
          F1event |          0  (omitted)
        ln_ew_ges |   1.678109   1.068642     1.57   0.117    -.4205078    3.776725
         ew_biodt |    .761748   .0314252    24.24   0.000     .7000348    .8234612
        ew_dtmihi |  -.1673742   .0522282    -3.20   0.001    -.2699407   -.0648076
         ew_ledig |   .4238555   .0699086     6.06   0.000     .2865679    .5611432
       ew_married |   .6387186   .0688582     9.28   0.000     .5034938    .7739433
        wb_anteil |  -.5320422   .0239517   -22.21   0.000     -.579079   -.4850055
          wb_ausl |  -.0535363   .0175921    -3.04   0.002     -.088084   -.0189885
         wb_18t24 |  -.0438039    .025903    -1.69   0.091    -.0946727    .0070648
         wb_25t34 |  -.0197863   .0166674    -1.19   0.236     -.052518    .0129454
         wb_35t44 |  -.0028286   .0208991    -0.14   0.892    -.0438706    .0382134
         wb_45t59 |  -.0241236   .0196706    -1.23   0.221    -.0627531    .0145059
          avg_dur |   .0170995    .022054     0.78   0.438    -.0262106    .0604096
          hh_kids |  -.1148866    .035755    -3.21   0.001    -.1851028   -.0446704
mpreis_flats_rent |   .0148646   .0236111     0.63   0.529    -.0315033    .0612326
            _cons |   -.366715   10.03193    -0.04   0.971    -20.06759    19.33416
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
.         // 50%+ reassignments                                                                   
>                                         
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                  reghdfe `v' F7event50-L7event50 F1event50 $ctr $wgt if smpl_trim50==1, absor
> b(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
  3.                 
.                 estimates store `v'_50
  4.         }       
(MWFE estimator converged in 8 iterations)
note: F1event50 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  28,    617) =      18.66
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9726
                                                  Adj R-squared   =     0.9663
                                                  Within R-sq.    =     0.1834
Number of clusters (sb_new)  =        618         Root MSE        =     1.6767

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event50 |   -.545539   .3049474    -1.79   0.074      -1.1444    .0533217
        F6event50 |  -.4106817   .2776704    -1.48   0.140    -.9559752    .1346119
        F5event50 |  -.1069107   .2328387    -0.46   0.646    -.5641631    .3503417
        F4event50 |  -.1732231   .1543964    -1.12   0.262    -.4764292     .129983
        F3event50 |  -.2678341   .1570532    -1.71   0.089    -.5762577    .0405895
        F2event50 |  -.0616308   .1138478    -0.54   0.588    -.2852069    .1619454
        L0event50 |  -1.225996   .2009633    -6.10   0.000    -1.620651   -.8313408
        L1event50 |  -1.398743   .2138324    -6.54   0.000    -1.818671   -.9788156
        L2event50 |  -1.169887   .2306288    -5.07   0.000    -1.622799   -.7169739
        L3event50 |  -.6769234   .2421256    -2.80   0.005    -1.152414   -.2014333
        L4event50 |  -.7907729   .5097147    -1.55   0.121    -1.791759    .2102132
        L5event50 |  -1.005058   .4690059    -2.14   0.033      -1.9261   -.0840171
        L6event50 |  -.6231133   .7177829    -0.87   0.386    -2.032707    .7864804
        L7event50 |  -.7475986   .6886611    -1.09   0.278    -2.100002    .6048053
        F1event50 |          0  (omitted)
        ln_ew_ges |  -1.191075   .9652402    -1.23   0.218    -3.086629    .7044796
         ew_biodt |   .3673334   .0286478    12.82   0.000     .3110744    .4235924
        ew_dtmihi |   .0568321   .0527826     1.08   0.282    -.0468232    .1604875
         ew_ledig |    .195397   .0569378     3.43   0.001     .0835815    .3072124
       ew_married |   .4240443   .0595731     7.12   0.000     .3070536    .5410349
        wb_anteil |    -.29419   .0208409   -14.12   0.000    -.3351176   -.2532624
          wb_ausl |   .0118955   .0164623     0.72   0.470    -.0204335    .0442245
         wb_18t24 |  -.0214181   .0294277    -0.73   0.467    -.0792087    .0363725
         wb_25t34 |  -.0682005   .0197567    -3.45   0.001    -.1069991   -.0294019
         wb_35t44 |   .0009117    .022741     0.04   0.968    -.0437474    .0455707
         wb_45t59 |   .0112834   .0223149     0.51   0.613     -.032539    .0551058
          avg_dur |  -.0247517   .0217509    -1.14   0.256    -.0674665    .0179631
          hh_kids |  -.0391756   .0423032    -0.93   0.355    -.1222513    .0439002
mpreis_flats_rent |   .0254196   .0260379     0.98   0.329     -.025714    .0765533
            _cons |   16.65751   9.440668     1.76   0.078    -1.882228    35.19725
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event50 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  28,    617) =      15.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9622
                                                  Adj R-squared   =     0.9536
                                                  Within R-sq.    =     0.2063
Number of clusters (sb_new)  =        618         Root MSE        =     1.6708

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event50 |   .5106243    .291137     1.75   0.080    -.0611153    1.082364
        F6event50 |   .6103819   .2413379     2.53   0.012     .1364386    1.084325
        F5event50 |  -.0423654   .2635833    -0.16   0.872    -.5599946    .4752639
        F4event50 |  -.0699138   .1482812    -0.47   0.637    -.3611108    .2212833
        F3event50 |   .0465007   .1380205     0.34   0.736    -.2245462    .3175476
        F2event50 |    -.07403   .1118647    -0.66   0.508    -.2937118    .1456517
        L0event50 |   .6722703   .1906548     3.53   0.000     .2978593    1.046681
        L1event50 |   1.117934    .209672     5.33   0.000     .7061767    1.529691
        L2event50 |   1.334885   .2285035     5.84   0.000     .8861461    1.783624
        L3event50 |   .7867692   .2493978     3.15   0.002     .2969977    1.276541
        L4event50 |   1.241805   .6021538     2.06   0.040      .059286    2.424325
        L5event50 |   1.531976   .5611425     2.73   0.007     .4299954    2.633957
        L6event50 |   .8809332   .7071438     1.25   0.213    -.5077674    2.269634
        L7event50 |    .866715   .6062442     1.43   0.153    -.3238373    2.057267
        F1event50 |          0  (omitted)
        ln_ew_ges |   3.249109   1.370681     2.37   0.018     .5573429    5.940876
         ew_biodt |   .3990626   .0309266    12.90   0.000     .3383284    .4597968
        ew_dtmihi |  -.2154219   .0607481    -3.55   0.000    -.3347199   -.0961239
         ew_ledig |   .2104666   .0844214     2.49   0.013     .0446784    .3762547
       ew_married |   .2002699   .0837115     2.39   0.017     .0358759    .3646638
        wb_anteil |  -.2389968   .0233566   -10.23   0.000    -.2848648   -.1931287
          wb_ausl |  -.0639241   .0144702    -4.42   0.000    -.0923409   -.0355072
         wb_18t24 |  -.0277554   .0279918    -0.99   0.322    -.0827261    .0272153
         wb_25t34 |   .0497139   .0194699     2.55   0.011     .0114787    .0879492
         wb_35t44 |  -.0057476   .0251587    -0.23   0.819    -.0551547    .0436594
         wb_45t59 |   -.032458   .0211496    -1.53   0.125    -.0739919    .0090758
          avg_dur |   .0409301   .0246966     1.66   0.098    -.0075694    .0894297
          hh_kids |  -.0743483   .0425813    -1.75   0.081    -.1579702    .0092736
mpreis_flats_rent |  -.0148301   .0242293    -0.61   0.541     -.062412    .0327517
            _cons |  -18.82342   11.61406    -1.62   0.106    -41.63129    3.984452
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event50 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  28,    617) =      41.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4316
Number of clusters (sb_new)  =        618         Root MSE        =     1.6262

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event50 |   -.034915   .2863399    -0.12   0.903     -.597234     .527404
        F6event50 |   .1997001   .2530485     0.79   0.430    -.2972407    .6966409
        F5event50 |   -.149276   .2494725    -0.60   0.550     -.639194    .3406421
        F4event50 |  -.2431365   .1460048    -1.67   0.096    -.5298632    .0435901
        F3event50 |  -.2213338   .1410567    -1.57   0.117    -.4983433    .0556758
        F2event50 |  -.1356606   .1249223    -1.09   0.278    -.3809851    .1096639
        L0event50 |  -.5537253   .1617312    -3.42   0.001    -.8713356    -.236115
        L1event50 |  -.2808089   .1900584    -1.48   0.140    -.6540486    .0924308
        L2event50 |   .1649985   .2180145     0.76   0.449     -.263142     .593139
        L3event50 |   .1098457   .2294935     0.48   0.632    -.3408373    .5605287
        L4event50 |    .451033   .4788523     0.94   0.347    -.4893449    1.391411
        L5event50 |   .5269186   .5681886     0.93   0.354    -.5888995    1.642737
        L6event50 |   .2578203   .7442588     0.35   0.729    -1.203767    1.719408
        L7event50 |   .1191168   .6758767     0.18   0.860    -1.208181    1.446414
        F1event50 |          0  (omitted)
        ln_ew_ges |   2.058034   1.228199     1.68   0.094    -.3539235    4.469992
         ew_biodt |    .766396    .032918    23.28   0.000      .701751     .831041
        ew_dtmihi |  -.1585897   .0541715    -2.93   0.004    -.2649726   -.0522067
         ew_ledig |   .4058637   .0723704     5.61   0.000     .2637415    .5479859
       ew_married |   .6243143     .07096     8.80   0.000     .4849619    .7636666
        wb_anteil |  -.5331867   .0252591   -21.11   0.000     -.582791   -.4835825
          wb_ausl |  -.0520286   .0176406    -2.95   0.003    -.0866715   -.0173856
         wb_18t24 |  -.0491735   .0268853    -1.83   0.068    -.1019713    .0036243
         wb_25t34 |  -.0184866   .0173672    -1.06   0.288    -.0525925    .0156194
         wb_35t44 |   -.004836   .0214908    -0.23   0.822      -.04704     .037368
         wb_45t59 |  -.0211746   .0205152    -1.03   0.302    -.0614626    .0191134
          avg_dur |   .0161784   .0230108     0.70   0.482    -.0290105    .0613674
          hh_kids |   -.113524   .0376914    -3.01   0.003     -.187543   -.0395049
mpreis_flats_rent |   .0105895   .0246889     0.43   0.668     -.037895     .059074
            _cons |   -2.16592   11.16675    -0.19   0.846    -24.09537    19.76353
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         // Bsl specification with rescaled ES dummies                                           
>                 
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                  reghdfe `v' F7eventRS-L7eventRS F1eventRS $ctr $wgt , absorb(i.wahl_id#i.sta
> dtbez i.sb_new) cluster(sb_new)
  3.                 
.                 estimates store `v'_rs
  4.         }       
(MWFE estimator converged in 5 iterations)
note: F1eventRS omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  28,    617) =      19.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9713
                                                  Adj R-squared   =     0.9655
                                                  Within R-sq.    =     0.1764
Number of clusters (sb_new)  =        618         Root MSE        =     1.7099

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7eventRS |  -.3531053   .3489138    -1.01   0.312    -1.038308    .3320973
        F6eventRS |  -.1760029   .3127551    -0.56   0.574    -.7901964    .4381907
        F5eventRS |   .1770064   .2547413     0.69   0.487    -.3232587    .6772714
        F4eventRS |    .015751   .1769272     0.09   0.929    -.3317016    .3632036
        F3eventRS |  -.0223402   .1758546    -0.13   0.899    -.3676864    .3230059
        F2eventRS |   .0717813    .123646     0.58   0.562    -.1710368    .3145994
        L0eventRS |  -1.138118   .2129324    -5.34   0.000    -1.556278   -.7199573
        L1eventRS |  -1.070042   .1999433    -5.35   0.000    -1.462694   -.6773907
        L2eventRS |  -.8295479   .2132226    -3.89   0.000    -1.248278   -.4108179
        L3eventRS |  -.4165818    .233352    -1.79   0.075    -.8748422    .0416787
        L4eventRS |  -.4748734   .5091618    -0.93   0.351    -1.474774    .5250268
        L5eventRS |  -.1659882    .521044    -0.32   0.750    -1.189223    .8572465
        L6eventRS |   .3381831   .6703508     0.50   0.614    -.9782627    1.654629
        L7eventRS |   .3853749   .7049196     0.55   0.585    -.9989577    1.769707
        F1eventRS |          0  (omitted)
        ln_ew_ges |  -.7204731   .9149978    -0.79   0.431    -2.517361    1.076415
         ew_biodt |   .3839959   .0267193    14.37   0.000     .3315242    .4364676
        ew_dtmihi |   .0556766   .0476382     1.17   0.243    -.0378761    .1492294
         ew_ledig |   .1806515   .0522544     3.46   0.001     .0780336    .2832695
       ew_married |   .3792022   .0541067     7.01   0.000     .2729465    .4854578
        wb_anteil |  -.2909761   .0200835   -14.49   0.000    -.3304164   -.2515358
          wb_ausl |   .0163967    .015287     1.07   0.284    -.0136242    .0464177
         wb_18t24 |  -.0284373   .0289055    -0.98   0.326    -.0852023    .0283278
         wb_25t34 |   -.067582   .0193294    -3.50   0.001    -.1055414   -.0296226
         wb_35t44 |   .0139344   .0223501     0.62   0.533    -.0299571    .0578259
         wb_45t59 |   .0093248   .0209751     0.44   0.657    -.0318664     .050516
          avg_dur |  -.0169732   .0212634    -0.80   0.425    -.0587306    .0247842
          hh_kids |  -.0448898   .0410854    -1.09   0.275     -.125574    .0357945
mpreis_flats_rent |   .0338102   .0228564     1.48   0.140    -.0110755    .0786959
            _cons |   13.44018   8.593207     1.56   0.118    -3.435303    30.31566
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)
note: F1eventRS omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  28,    617) =      18.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9598
                                                  Adj R-squared   =     0.9516
                                                  Within R-sq.    =     0.2103
Number of clusters (sb_new)  =        618         Root MSE        =     1.6990

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7eventRS |   .3530824    .329229     1.07   0.284    -.2934628    .9996275
        F6eventRS |    .380188   .2729559     1.39   0.164    -.1558471    .9162232
        F5eventRS |  -.1871213   .2605597    -0.72   0.473    -.6988128    .3245701
        F4eventRS |  -.1208035   .1611342    -0.75   0.454    -.4372415    .1956345
        F3eventRS |  -.0117797   .1524101    -0.08   0.938    -.3110852    .2875259
        F2eventRS |   -.057502   .1221471    -0.47   0.638    -.2973764    .1823724
        L0eventRS |   .6750189   .2003945     3.37   0.001     .2814809    1.068557
        L1eventRS |    .972373   .1981225     4.91   0.000     .5832969    1.361449
        L2eventRS |   1.056812   .2236621     4.73   0.000     .6175803    1.496043
        L3eventRS |   .6239405   .2468879     2.53   0.012      .139098    1.108783
        L4eventRS |   1.180393   .5824357     2.03   0.043     .0365967     2.32419
        L5eventRS |   1.368773   .5295124     2.58   0.010     .3289083    2.408638
        L6eventRS |   .5575799   .7150645     0.78   0.436    -.8466753    1.961835
        L7eventRS |   .7956564   .6698774     1.19   0.235    -.5198598    2.111173
        F1eventRS |          0  (omitted)
        ln_ew_ges |   2.321696   1.124997     2.06   0.039      .112409    4.530982
         ew_biodt |   .3881463   .0286733    13.54   0.000     .3318372    .4444555
        ew_dtmihi |  -.2129794   .0553549    -3.85   0.000    -.3216861   -.1042726
         ew_ledig |   .2411033   .0625485     3.85   0.000     .1182695    .3639371
       ew_married |   .2786401   .0633527     4.40   0.000     .1542271    .4030532
        wb_anteil |  -.2514391   .0227173   -11.07   0.000    -.2960517   -.2068265
          wb_ausl |  -.0769135   .0147589    -5.21   0.000    -.1058974   -.0479297
         wb_18t24 |    -.01006   .0264487    -0.38   0.704    -.0620004    .0418805
         wb_25t34 |   .0519222   .0184133     2.82   0.005      .015762    .0880825
         wb_35t44 |  -.0173539   .0238839    -0.73   0.468    -.0642576    .0295497
         wb_45t59 |  -.0406786   .0194082    -2.10   0.036    -.0787927   -.0025645
          avg_dur |   .0348938   .0232008     1.50   0.133    -.0106683    .0804559
          hh_kids |  -.0758344   .0386512    -1.96   0.050    -.1517383    .0000696
mpreis_flats_rent |  -.0110615   .0216974    -0.51   0.610    -.0536712    .0315482
            _cons |  -13.72564   9.619124    -1.43   0.154    -32.61583    5.164551
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)
note: F1eventRS omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  28,    617) =      49.29
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9898
                                                  Adj R-squared   =     0.9877
                                                  Within R-sq.    =     0.4478
Number of clusters (sb_new)  =        618         Root MSE        =     1.6446

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7eventRS |  -.0000227   .3301229    -0.00   1.000    -.6483235    .6482781
        F6eventRS |   .2041847   .2776105     0.74   0.462    -.3409912    .7493607
        F5eventRS |  -.0101145   .2653404    -0.04   0.970    -.5311942    .5109653
        F4eventRS |  -.1050521   .1688892    -0.62   0.534    -.4367194    .2266152
        F3eventRS |  -.0341201   .1565165    -0.22   0.828    -.3414897    .2732494
        F2eventRS |   .0142798    .131274     0.11   0.913    -.2435182    .2720777
        L0eventRS |  -.4630984   .1593483    -2.91   0.004    -.7760291   -.1501676
        L1eventRS |  -.0976692     .18793    -0.52   0.603    -.4667291    .2713907
        L2eventRS |   .2272639   .2066187     1.10   0.272    -.1784974    .6330251
        L3eventRS |   .2073591   .2318216     0.89   0.371     -.247896    .6626141
        L4eventRS |   .7055203   .4935429     1.43   0.153    -.2637073    1.674748
        L5eventRS |   1.202785   .5047123     2.38   0.017     .2116224    2.193947
        L6eventRS |   .8957639   .6743101     1.33   0.185    -.4284572    2.219985
        L7eventRS |   1.181033   .6585759     1.79   0.073    -.1122892    2.474355
        F1eventRS |          0  (omitted)
        ln_ew_ges |   1.601223   1.015858     1.58   0.115    -.3937351    3.596181
         ew_biodt |   .7721422   .0307416    25.12   0.000     .7117714    .8325131
        ew_dtmihi |  -.1573026   .0504105    -3.12   0.002    -.2562995   -.0583057
         ew_ledig |   .4217549   .0612753     6.88   0.000     .3014216    .5420883
       ew_married |   .6578423   .0600129    10.96   0.000     .5399879    .7756967
        wb_anteil |  -.5424152   .0239134   -22.68   0.000    -.5893766   -.4954537
          wb_ausl |  -.0605168   .0177256    -3.41   0.001    -.0953266    -.025707
         wb_18t24 |  -.0384972   .0254364    -1.51   0.131    -.0884497    .0114553
         wb_25t34 |  -.0156598   .0166919    -0.94   0.349    -.0484397    .0171202
         wb_35t44 |  -.0034194   .0200847    -0.17   0.865    -.0428622    .0360233
         wb_45t59 |  -.0313538   .0192295    -1.63   0.104     -.069117    .0064094
          avg_dur |   .0179206   .0214694     0.83   0.404    -.0242414    .0600827
          hh_kids |  -.1207242   .0344176    -3.51   0.000    -.1883141   -.0531344
mpreis_flats_rent |   .0227487   .0223036     1.02   0.308    -.0210515    .0665489
            _cons |  -.2854756   9.284755    -0.03   0.975    -18.51903    17.94808
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
.         // dCdH (2023):  CONT TREATMENT
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                 did_multiplegt_dyn `v' sb_new wahl_id  treat_simple_clean ,  placebo(3) effects(
> 3)   cluster(sb_new)  controls(ctf* $ctr) weight(wahlber_gesamt) graph_off
  3.                 
.                 // storing the estimates
.                 matrix dcdh_b_`v' = e(estimates) 
  4.                 matrix dcdh_v_`v' = e(variances)        
  5.                 matrix  rownames dcdh_b_`v' =Effect_0 Effect_1 Effect_2 Av_tot_eff Placebo_2 
> Placebo_3 Placebo_4 Placebo_1
  6.                 matrix  rownames dcdh_v_`v' =Effect_0 Effect_1 Effect_2 Av_tot_eff Placebo_2 
> Placebo_3 Placebo_4 Placebo_1      
  7.         }       
Some control variables are not taken into account for groups with baseline treatment equal to: [0]
This may occur in the following situations:
1. For groups with those values of the baseline treatment,
the regression of the outcome first difference on the controls' first differences 
and time fixed effects has fewer observations than variables.
Note that for each value of the baseline treatment,
those regressions are estimated among (g,t)s such that g has not changed treatment yet at t.
2. For groups with those values of the baseline treatment, 
two or more of your control variables are perfectly collinear 
in the sample where the regression is run, for instance because those control variables do not var
> y over time.


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -.7488387   .2104967  -1.161412  -.3362652    3164192     775705 
    Effect_2 | -1.080768     .23829  -1.547816  -.6137197    2323116   524390.5 
    Effect_3 |  -.825007   .2247902  -1.265596  -.3844181    1502796   517828.6 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff | -1.117936   .2536954  -1.615179  -.6206927    5079177    1817924            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  .0998405   .1253196   -.145786    .345467    3164192     775705 
   Placebo_2 | -.0951836     .22217  -.5306367   .3402696    1433524   507563.1 
   Placebo_3 |   -.16584   .2268609  -.6104874   .2788074    1050833   501428.6 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .59873537


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).
Some control variables are not taken into account for groups with baseline treatment equal to: [0]
This may occur in the following situations:
1. For groups with those values of the baseline treatment,
the regression of the outcome first difference on the controls' first differences 
and time fixed effects has fewer observations than variables.
Note that for each value of the baseline treatment,
those regressions are estimated among (g,t)s such that g has not changed treatment yet at t.
2. For groups with those values of the baseline treatment, 
two or more of your control variables are perfectly collinear 
in the sample where the regression is run, for instance because those control variables do not var
> y over time.


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 |  .4510021   .2057193   .0477923    .854212    3164192     775705 
    Effect_2 |   .914803   .2442288   .4361145   1.393491    2323116   524390.5 
    Effect_3 |  .9174685   .2627093   .4025583   1.432379    1502796   517828.6 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  .9261383   .2712638   .3944614   1.457815    5079177    1817924            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 | -.0644722   .1196899  -.2990643     .17012    3164192     775705 
   Placebo_2 | -.2132353   .1868702   -.579501   .1530303    1433524   507563.1 
   Placebo_3 | -.1993966   .1524937  -.4982843   .0994911    1050833   501428.6 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .56894419


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).
Some control variables are not taken into account for groups with baseline treatment equal to: [0]
This may occur in the following situations:
1. For groups with those values of the baseline treatment,
the regression of the outcome first difference on the controls' first differences 
and time fixed effects has fewer observations than variables.
Note that for each value of the baseline treatment,
those regressions are estimated among (g,t)s such that g has not changed treatment yet at t.
2. For groups with those values of the baseline treatment, 
two or more of your control variables are perfectly collinear 
in the sample where the regression is run, for instance because those control variables do not var
> y over time.


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -.2978364    .197951  -.6858204   .0901476    3164192     775705 
    Effect_2 | -.1659649   .2480743  -.6521906   .3202608    2323116   524390.5 
    Effect_3 |  .0924619   .2742946  -.4451555   .6300794    1502796   517828.6 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff | -.1917971   .2644399  -.7100993   .3265051    5079177    1817924            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  .0353685   .1444556  -.2477645   .3185015    3164192     775705 
   Placebo_2 | -.3084193   .2000334  -.7004848   .0836461    1433524   507563.1 
   Placebo_3 | -.3652362   .2036334  -.7643577   .0338853    1050833   501428.6 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .2271728


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).

.         
. ** PLOT         
.         // legend
.         local legd `"order(1 "Binary treatment: full reassignments (Baseline)" 3 "Binary treatme
> nt: 50%+ addresses reassigned" 5 "Continuous treatment: rescaled event-time dummies" 7 "Continuo
> us treatment (deChaisemartin et al, 2023)" )"'

.         
.         // PLOT: Turnouts Urne 
.         local d = "turnout_urne" 

.         event_plot   `d'_bsl `d'_50 `d'_rs dcdh_b_`d'#dcdh_v_`d', ///
>         stub_lag(L#event L#event50 L#eventRS Effect_#) stub_lead(F#event F#event50 F#eventRS Pla
> cebo_#) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4(1)2) xtitl
> e("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `legd' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel A.} Effect on Polling Place Turnout",nobox span bexpand justifi
> cation(left) size(medium)) ///
>                 name(urne, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry))  /
> //
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))

.         
.         // PLOT: Turnouts Postal        
.         local d = "turnout_pos_req" 

.         event_plot   `d'_bsl `d'_50 `d'_rs dcdh_b_`d'#dcdh_v_`d' , ///
>         stub_lag(L#event L#event50 L#eventRS Effect_#) stub_lead(F#event F#event50 F#eventRS Pla
> cebo_#) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4(1)2) xtitl
> e("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `legd' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel B.} Effect on Mail-in Turnout",nobox span bexpand justification
> (left) size(medium)) ///
>                 name(postal, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry))  /
> //
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))

. 
.         // PLOT: Turnouts Total
.         local d = "turnout_tot_req" 

.         event_plot   `d'_bsl `d'_50 `d'_rs dcdh_b_`d'#dcdh_v_`d', ///
>         stub_lag(L#event L#event50 L#eventRS Effect_#) stub_lead(F#event F#event50 F#eventRS Pla
> cebo_#) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4(1)2) xtitl
> e("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `legd' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel C.} Effect on Total Turnout",nobox span bexpand justification(l
> eft) size(medium)) ///
>                 name(total, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry))  /
> //
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))

.         
.                 
.                 * PLOT: FIGURE C8. Robustness to Alternative Treatment Definitions–Pooled Reassi
> gnments
.                 grc1leg2  urne postal total , xcommon  col(2) iscale(.7) name(bsl_rob_alt, repla
> ce) ///
>                 pos(4) ring(0) lcol(1) lxoffset(0) lyoffset(15) legscale(*.9) 
-grc1leg2- working...

Note: To preserve in a saved graphics file the legend's row and column rearrangements
made with the options -lrows()- and/or -lcols()-, specify the suboption -asis-
for the saving() option or for the graph save command.


.                 gr_edit .legend.title.DragBy 0 -15

.                 graph export "$figures/Figure_C8_ES_alternative_treatm_bsl.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C8_ES_
    > alternative_treatm_bsl.pdf saved as PDF format

. 
.                 
. ********************************************************************************
. // Binary T (100%), binary T (50%+), cont. T (TWFE), cont. T (dC et al, 2023)  //
. // -> Reassignments by distance increase/decrease (Figure C9)
. ********************************************************************************                
. 
.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_dn                            // a 
> := decrease
  3.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b = F`l'event *ind_dist_up                         
>    // b:= increase
  5.                 lab var F`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6. 
.                 assert  F`l'event_b+F`l'event_a==F`l'event              
  7.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_dn    // a := decrease
  3.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b = L`l'event *ind_dist_up    // b:= increase
  5.                 lab var L`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 assert  L`l'event_b+L`l'event_a==L`l'event
  7.         }               

.         
.         // ORDER dummies
.         order *event_b, last

.         order F1event*,last             

. 
. * ES dummies for treat=50%+
.         // Create two set of dummies: Dist Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a50 = F`l'event50 *ind_dist_dn50                           
>    // a := decrease
  3.                 lab var F`l'event_a50 "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b50 = F`l'event50 *ind_dist_up50                   
>            // b:= increase
  5.                 lab var F`l'event_b50 "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6. 
.                 assert  F`l'event_b50+F`l'event_a50==F`l'event50                
  7.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a50 = L`l'event50 *ind_dist_dn50      // a := decrease
  3.                 lab var L`l'event_a50 "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b50 = L`l'event50 *ind_dist_up50      // b:= increa
> se
  5.                 lab var L`l'event_b50 "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 assert  L`l'event_b50+L`l'event_a50==L`l'event50
  7.         }               

.         
.         // ORDER dummies
.         order *event_b50, last

.         order F1event50*,last                           

.         
. * Rescaled dummies
.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_aX = F`l'eventX *ind_dist_dnX  *treat_simple_max //*del_str
> eet_dist_max       // a := decrease
  3.                 lab var F`l'event_aX "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_bX = F`l'eventX *ind_dist_upX  *treat_simple_max //
> *del_street_dist_max       // b:= increase
  5.                 lab var F`l'event_bX "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6.                 *assert  F`l'event_bX+F`l'event_aX==F`l'eventX
.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_aX = L`l'eventX *ind_dist_dnX  *treat_simple_max //*del_str
> eet_dist_max // a := decrease
  3.                 lab var L`l'event_aX "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_bX = L`l'eventX *ind_dist_upX *treat_simple_max //*
> del_street_dist_max        // b:= increase
  5.                 lab var L`l'event_bX "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 *assert  L`l'event_bX+L`l'event_aX==L`l'eventX
.         }               

.         
.         // ORDER dummies
.         order *event_bX, last

.         order F1event*,last     

.         
.         
.         // Estimate ES by increase/decrease in distance
.         estimates clear

.         
. 
.         // Baseline
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                  reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b $ctr $
> wgt ///
>                                         if  smpl_trim==1  , absorb(i.wahl_id#i.stadtbez i.sb_new
> ) cluster(sb_new)
  3. 
.                 estimates store `v'_a_bsl
  4.                 estimates store `v'_b_bsl
  5.         }       
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      14.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9734
                                                  Adj R-squared   =     0.9674
                                                  Within R-sq.    =     0.2120
Number of clusters (sb_new)  =        618         Root MSE        =     1.6565

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .3228052   .5620586     0.57   0.566    -.7809746    1.426585
        F6event_a |   -.008119   .5445209    -0.01   0.988    -1.077458     1.06122
        F5event_a |   .2401916    .384451     0.62   0.532    -.5147995    .9951827
        F4event_a |   -.156618   .2415581    -0.65   0.517    -.6309938    .3177578
        F3event_a |  -.2497959    .237662    -1.05   0.294    -.7165204    .2169286
        F2event_a |  -.2004224   .1774212    -1.13   0.259    -.5488451    .1480002
        L0event_a |    .475771   .3448323     1.38   0.168    -.2014161    1.152958
        L1event_a |   .6029534   .3142192     1.92   0.055    -.0141153    1.220022
        L2event_a |    .489146   .3473889     1.41   0.160     -.193062    1.171354
        L3event_a |    .768641   .3520682     2.18   0.029     .0772438    1.460038
        L4event_a |   .2930804   .6191775     0.47   0.636    -.9228704    1.509031
        L5event_a |   .5968234   1.001193     0.60   0.551    -1.369335    2.562982
        L6event_a |   2.971707   1.125918     2.64   0.009     .7606118    5.182803
        L7event_a |   .4542621    .595416     0.76   0.446    -.7150254     1.62355
        F7event_b |   -.458259   .4061302    -1.13   0.260    -1.255824    .3393062
        F6event_b |   .0370394   .3490613     0.11   0.916    -.6484527    .7225316
        F5event_b |   .1948103   .2958098     0.66   0.510    -.3861058    .7757263
        F4event_b |   .1010004   .2103875     0.48   0.631     -.312162    .5141628
        F3event_b |    .051904   .2046495     0.25   0.800    -.3499901     .453798
        F2event_b |   .1484136   .1455165     1.02   0.308    -.1373541    .4341812
        L0event_b |  -1.892486   .2685797    -7.05   0.000    -2.419927   -1.365045
        L1event_b |  -1.964155   .2722347    -7.21   0.000    -2.498774   -1.429536
        L2event_b |   -1.59154    .310441    -5.13   0.000    -2.201189   -.9818908
        L3event_b |  -1.046249   .3334843    -3.14   0.002    -1.701151   -.3913475
        L4event_b |  -1.513942   .5864355    -2.58   0.010    -2.665594   -.3622908
        L5event_b |  -1.192543   .6858851    -1.74   0.083    -2.539496     .154409
        L6event_b |  -.5363127   .5611732    -0.96   0.340    -1.638354    .5657283
        L7event_b |   1.781413   1.393593     1.28   0.202    -.9553482    4.518174
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -.9961214   .9212525    -1.08   0.280    -2.805292    .8130493
         ew_biodt |   .3683074   .0277532    13.27   0.000     .3138051    .4228097
        ew_dtmihi |   .0672716   .0499711     1.35   0.179    -.0308624    .1654056
         ew_ledig |   .2075291   .0540848     3.84   0.000     .1013165    .3137417
       ew_married |   .4088781   .0556124     7.35   0.000     .2996655    .5180906
        wb_anteil |   -.284197   .0201233   -14.12   0.000    -.3237154   -.2446785
          wb_ausl |    .016981   .0158374     1.07   0.284    -.0141208    .0480829
         wb_18t24 |  -.0166004   .0296128    -0.56   0.575    -.0747545    .0415537
         wb_25t34 |  -.0623142   .0187354    -3.33   0.001    -.0991071   -.0255213
         wb_35t44 |   .0057088   .0225147     0.25   0.800    -.0385059    .0499234
         wb_45t59 |   .0149036   .0214535     0.69   0.488    -.0272271    .0570344
          avg_dur |  -.0226264   .0201538    -1.12   0.262    -.0622046    .0169519
          hh_kids |  -.0391984   .0390032    -1.01   0.315    -.1157936    .0373968
mpreis_flats_rent |   .0286627   .0245902     1.17   0.244     -.019628    .0769534
            _cons |   13.43092   8.531997     1.57   0.116    -3.324354     30.1862
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      14.90
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9628
                                                  Adj R-squared   =     0.9544
                                                  Within R-sq.    =     0.2284
Number of clusters (sb_new)  =        618         Root MSE        =     1.6618

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.7726385   .5349539    -1.44   0.149     -1.82319    .2779128
        F6event_a |  -.1638495     .45516    -0.36   0.719      -1.0577    .7300012
        F5event_a |  -1.068847   .4042341    -2.64   0.008    -1.862688   -.2750054
        F4event_a |  -.2732057   .2186614    -1.25   0.212    -.7026165    .1562052
        F3event_a |   .1422311   .2194149     0.65   0.517    -.2886594    .5731216
        F2event_a |  -.0493139    .179667    -0.27   0.784    -.4021469    .3035192
        L0event_a |  -.4599664   .3083689    -1.49   0.136    -1.065546    .1456135
        L1event_a |  -.3946958   .3108984    -1.27   0.205    -1.005243    .2158516
        L2event_a |   .0490141   .3556253     0.14   0.890    -.6493687    .7473968
        L3event_a |  -.6600526   .3220275    -2.05   0.041    -1.292455   -.0276498
        L4event_a |  -.1714753   .9749698    -0.18   0.860    -2.086137    1.743186
        L5event_a |   1.330135   .8766554     1.52   0.130    -.3914553    3.051725
        L6event_a |  -2.832304   .6886765    -4.11   0.000    -4.184738    -1.47987
        L7event_a |  -1.841205   .6807652    -2.70   0.007    -3.178103   -.5043073
        F7event_b |   .5841825   .3298669     1.77   0.077    -.0636156    1.231981
        F6event_b |   .4327823   .2632287     1.64   0.101    -.0841505     .949715
        F5event_b |  -.1844277   .3115985    -0.59   0.554    -.7963498    .4274945
        F4event_b |  -.1933853   .1940332    -1.00   0.319    -.5744308    .1876602
        F3event_b |  -.0668334   .1799966    -0.37   0.711    -.4203135    .2866468
        F2event_b |  -.0883951   .1461363    -0.60   0.545      -.37538    .1985897
        L0event_b |   1.258426   .2583978     4.87   0.000     .7509797    1.765871
        L1event_b |   1.819219   .2672851     6.81   0.000      1.29432    2.344118
        L2event_b |   1.723036   .3262228     5.28   0.000     1.082394    2.363677
        L3event_b |   1.207218   .3296335     3.66   0.000     .5598788    1.854558
        L4event_b |   2.900775    .518063     5.60   0.000     1.883394    3.918155
        L5event_b |    2.93048   .5822414     5.03   0.000     1.787065    4.073896
        L6event_b |   1.816891   .6623496     2.74   0.006      .516158    3.117624
        L7event_b |    .546884   1.228547     0.45   0.656    -1.865757    2.959525
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.622643   1.331899     1.97   0.049      .007038    5.238248
         ew_biodt |    .390918   .0287073    13.62   0.000     .3345422    .4472938
        ew_dtmihi |  -.2363622   .0597435    -3.96   0.000    -.3536875   -.1190369
         ew_ledig |   .2132309   .0770102     2.77   0.006      .061997    .3644649
       ew_married |   .2221102   .0771453     2.88   0.004      .070611    .3736095
        wb_anteil |  -.2460859   .0218023   -11.29   0.000    -.2889017   -.2032702
          wb_ausl |  -.0699769    .014375    -4.87   0.000    -.0982067   -.0417471
         wb_18t24 |  -.0295899   .0273374    -1.08   0.279    -.0832755    .0240957
         wb_25t34 |   .0438136   .0190773     2.30   0.022     .0063493    .0812779
         wb_35t44 |  -.0081487    .024216    -0.34   0.737    -.0557044     .039407
         wb_45t59 |  -.0389038    .020268    -1.92   0.055    -.0787064    .0008987
          avg_dur |   .0429859   .0224788     1.91   0.056    -.0011584    .0871302
          hh_kids |  -.0731669   .0409186    -1.79   0.074    -.1535236    .0071898
mpreis_flats_rent |  -.0170848   .0235218    -0.73   0.468    -.0632773    .0291077
            _cons |  -12.97282   11.12696    -1.17   0.244    -34.82413     8.87849
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,666
Absorbing 2 HDFE groups                           F(  42,    617) =      33.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4440
Number of clusters (sb_new)  =        618         Root MSE        =     1.6210

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.4498347   .4925722    -0.91   0.361    -1.417156    .5174865
        F6event_a |  -.1719699   .3902489    -0.44   0.660     -.938347    .5944072
        F5event_a |   -.828655   .4505411    -1.84   0.066    -1.713435    .0561249
        F4event_a |  -.4298239   .2426357    -1.77   0.077    -.9063158     .046668
        F3event_a |  -.1075655   .2168072    -0.50   0.620    -.5333349     .318204
        F2event_a |  -.2497362   .2019212    -1.24   0.217    -.6462722    .1467999
        L0event_a |   .0158041    .239823     0.07   0.947    -.4551642    .4867724
        L1event_a |   .2082574   .2812297     0.74   0.459     -.344026    .7605409
        L2event_a |   .5381604   .3069533     1.75   0.080    -.0646395     1.14096
        L3event_a |   .1085883   .2874196     0.38   0.706     -.455851    .6730276
        L4event_a |   .1216059   .9220699     0.13   0.895     -1.68917    1.932382
        L5event_a |   1.926959   1.282873     1.50   0.134    -.5923686    4.446286
        L6event_a |   .1394028   1.303699     0.11   0.915    -2.420823    2.699628
        L7event_a |  -1.386941   .8623393    -1.61   0.108    -3.080417    .3065348
        F7event_b |   .1259241   .3695883     0.34   0.733    -.5998794    .8517276
        F6event_b |   .4698213   .3243514     1.45   0.148    -.1671453    1.106788
        F5event_b |   .0103831   .2850784     0.04   0.971    -.5494586    .5702248
        F4event_b |  -.0923843    .199051    -0.46   0.643    -.4832838    .2985153
        F3event_b |  -.0149294    .194926    -0.08   0.939    -.3977282    .3678695
        F2event_b |   .0600191   .1591538     0.38   0.706    -.2525298     .372568
        L0event_b |  -.6340596   .2007002    -3.16   0.002    -1.028198   -.2399212
        L1event_b |   -.144935    .252047    -0.58   0.565    -.6399089     .350039
        L2event_b |   .1314959    .278574     0.47   0.637    -.4155722    .6785641
        L3event_b |   .1609696   .3069884     0.52   0.600    -.4418993    .7638384
        L4event_b |   1.386832   .5405588     2.57   0.011     .3252744    2.448391
        L5event_b |   1.737936   .6427991     2.70   0.007     .4755971    3.000276
        L6event_b |   1.280575   .5287144     2.42   0.016     .2422772    2.318873
        L7event_b |   2.328298   .4952053     4.70   0.000     1.355806     3.30079
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.626521   1.066654     1.52   0.128    -.4681905    3.721233
         ew_biodt |   .7592254   .0315323    24.08   0.000     .6973017    .8211491
        ew_dtmihi |  -.1690904   .0518885    -3.26   0.001    -.2709899    -.067191
         ew_ledig |   .4207602   .0705442     5.96   0.000     .2822243    .5592962
       ew_married |   .6309884   .0690977     9.13   0.000     .4952933    .7666835
        wb_anteil |  -.5302829   .0240672   -22.03   0.000    -.5775465   -.4830192
          wb_ausl |  -.0529959   .0176265    -3.01   0.003     -.087611   -.0183807
         wb_18t24 |  -.0461903   .0262697    -1.76   0.079    -.0977791    .0053986
         wb_25t34 |  -.0185006   .0166385    -1.11   0.267    -.0511756    .0141745
         wb_35t44 |  -.0024399   .0210884    -0.12   0.908    -.0438536    .0389737
         wb_45t59 |  -.0240002    .019559    -1.23   0.220    -.0624104    .0144101
          avg_dur |   .0203595   .0222527     0.91   0.361    -.0233406    .0640597
          hh_kids |  -.1123654   .0358261    -3.14   0.002    -.1827212   -.0420096
mpreis_flats_rent |   .0115779   .0234248     0.49   0.621    -.0344241    .0575799
            _cons |   .4580862   9.951597     0.05   0.963    -19.08502     20.0012
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                 
.         
.         // 50%+ treatment                                                                       
>                                                 
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                 reghdfe `v' F7event_a50-L7event_a50  F7event_b50-L7event_b50 F1event_a50 F1event
> _b50 $ctr $wgt ///
>                                         if  smpl_trim50==1 , absorb(i.wahl_id#i.stadtbez i.sb_ne
> w) cluster(sb_new)
  3. 
.                 estimates store `v'_a_50
  4.                 estimates store `v'_b_50
  5.         }       
(MWFE estimator converged in 8 iterations)
note: F1event_a50 omitted because of collinearity
note: F1event_b50 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  42,    617) =      15.05
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9744
                                                  Adj R-squared   =     0.9684
                                                  Within R-sq.    =     0.2367
Number of clusters (sb_new)  =        618         Root MSE        =     1.6242

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
      F7event_a50 |  -.2425461   .4431461    -0.55   0.584    -1.112804    .6277114
      F6event_a50 |  -.4722585   .4255797    -1.11   0.268    -1.308019    .3635018
      F5event_a50 |   .2098925   .3591174     0.58   0.559    -.4953482    .9151332
      F4event_a50 |  -.2933135   .2205554    -1.33   0.184    -.7264437    .1398168
      F3event_a50 |  -.2879986   .2378959    -1.21   0.227    -.7551824    .1791852
      F2event_a50 |  -.0545839    .166921    -0.33   0.744     -.382386    .2732183
      L0event_a50 |   .3243222   .3064914     1.06   0.290    -.2775707     .926215
      L1event_a50 |   .2699224   .2945721     0.92   0.360    -.3085631    .8484079
      L2event_a50 |   .2201585   .3293394     0.67   0.504    -.4266034    .8669205
      L3event_a50 |   .7504388   .2961711     2.53   0.012     .1688132    1.332064
      L4event_a50 |   .6831541   .5885559     1.16   0.246    -.4726614     1.83897
      L5event_a50 |   .5637001   .8162569     0.69   0.490    -1.039279    2.166679
      L6event_a50 |    1.20381   .5759146     2.09   0.037     .0728199    2.334801
      L7event_a50 |   .5624557   .6606802     0.85   0.395    -.7349989     1.85991
      F7event_b50 |  -.6578895   .3499319    -1.88   0.061    -1.345091    .0293124
      F6event_b50 |  -.3642862   .3147663    -1.16   0.248    -.9824294     .253857
      F5event_b50 |  -.2622655   .2627724    -1.00   0.319    -.7783022    .2537713
      F4event_b50 |  -.1039385   .1784858    -0.58   0.561    -.4544519    .2465749
      F3event_b50 |  -.2567186   .1754014    -1.46   0.144    -.6011748    .0877376
      F2event_b50 |  -.0366074   .1312268    -0.28   0.780    -.2943127    .2210979
      L0event_b50 |  -2.106701   .2213055    -9.52   0.000    -2.541305   -1.672098
      L1event_b50 |  -2.495562   .2409648   -10.36   0.000    -2.968773   -2.022352
      L2event_b50 |  -2.097913   .2620795    -8.00   0.000    -2.612589   -1.583237
      L3event_b50 |  -1.608495   .3054481    -5.27   0.000    -2.208339   -1.008651
      L4event_b50 |  -1.642703   .6899524    -2.38   0.018    -2.997643   -.2877636
      L5event_b50 |  -1.899677   .5079235    -3.74   0.000    -2.897145   -.9022082
      L6event_b50 |  -1.892438    .965276    -1.96   0.050    -3.788062    .0031871
      L7event_b50 |  -1.526047   .9771737    -1.56   0.119    -3.445036     .392943
      F1event_a50 |          0  (omitted)
      F1event_b50 |          0  (omitted)
        ln_ew_ges |   -1.43711   .8854447    -1.62   0.105    -3.175961    .3017405
         ew_biodt |   .3789297   .0277883    13.64   0.000     .3243586    .4335009
        ew_dtmihi |   .0448777   .0506409     0.89   0.376    -.0545718    .1443272
         ew_ledig |   .1853485   .0514906     3.60   0.000     .0842304    .2864666
       ew_married |    .396975   .0538498     7.37   0.000     .2912239    .5027261
        wb_anteil |  -.2909815   .0205249   -14.18   0.000    -.3312887   -.2506743
          wb_ausl |   .0125388    .016042     0.78   0.435    -.0189647    .0440423
         wb_18t24 |  -.0257144    .028805    -0.89   0.372    -.0822822    .0308534
         wb_25t34 |  -.0507354   .0187135    -2.71   0.007    -.0874853   -.0139854
         wb_35t44 |   .0050032   .0222763     0.22   0.822    -.0387433    .0487498
         wb_45t59 |   .0119877   .0212797     0.56   0.573    -.0298017    .0537772
          avg_dur |   -.021254   .0205331    -1.04   0.301    -.0615772    .0190692
          hh_kids |  -.0230126    .040225    -0.57   0.567    -.1020071    .0559818
mpreis_flats_rent |   .0238489   .0248446     0.96   0.337    -.0249414    .0726392
            _cons |   18.59255   8.565478     2.17   0.030      1.77153    35.41358
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a50 omitted because of collinearity
note: F1event_b50 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  42,    617) =      11.98
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9635
                                                  Adj R-squared   =     0.9550
                                                  Within R-sq.    =     0.2326
Number of clusters (sb_new)  =        618         Root MSE        =     1.6460

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
      F7event_a50 |  -.3710356   .4815152    -0.77   0.441    -1.316643    .5745718
      F6event_a50 |   .1458537   .4334842     0.34   0.737    -.7054296    .9971369
      F5event_a50 |   -.724671   .4175097    -1.74   0.083    -1.544583    .0952414
      F4event_a50 |  -.1525882   .2024832    -0.75   0.451     -.550228    .2450515
      F3event_a50 |   .0960778   .2037985     0.47   0.637    -.3041451    .4963006
      F2event_a50 |  -.1116089   .1595308    -0.70   0.484    -.4248981    .2016802
      L0event_a50 |  -.3351952   .2738147    -1.22   0.221     -.872917    .2025266
      L1event_a50 |   -.165668   .2926055    -0.57   0.571    -.7402914    .4089554
      L2event_a50 |   .4463768   .3200038     1.39   0.164    -.1820519    1.074805
      L3event_a50 |  -.3016832   .3215848    -0.94   0.349    -.9332166    .3298502
      L4event_a50 |  -.2089261   .8908432    -0.23   0.815    -1.958378    1.540526
      L5event_a50 |   .3371243   1.067456     0.32   0.752    -1.759164    2.433412
      L6event_a50 |  -1.441837   1.094952    -1.32   0.188    -3.592121    .7084477
      L7event_a50 |  -1.046492   .7136679    -1.47   0.143    -2.448005    .3550206
      F7event_b50 |   .9556733   .2958758     3.23   0.001     .3746276    1.536719
      F6event_b50 |   .8187654   .2582826     3.17   0.002     .3115457    1.325985
      F5event_b50 |   .2395711   .2986339     0.80   0.423     -.346891    .8260332
      F4event_b50 |  -.0252963   .1690783    -0.15   0.881    -.3573351    .3067425
      F3event_b50 |   .0210546   .1556955     0.14   0.892    -.2847026    .3268119
      F2event_b50 |   -.086041   .1261345    -0.68   0.495    -.3337459    .1616639
      L0event_b50 |   1.257406   .2260096     5.56   0.000      .813565    1.701247
      L1event_b50 |   1.959666   .2460266     7.97   0.000     1.476515    2.442817
      L2event_b50 |   1.932492   .2804867     6.89   0.000     1.381668    2.483317
      L3event_b50 |   1.505046   .3074205     4.90   0.000     .9013291    2.108764
      L4event_b50 |   2.187042   .7540855     2.90   0.004     .7061571    3.667928
      L5event_b50 |   2.221347   .6080318     3.65   0.000     1.027284     3.41541
      L6event_b50 |   2.317133   .7372921     3.14   0.002     .8692269    3.765039
      L7event_b50 |   2.031562   .7245689     2.80   0.005     .6086416    3.454482
      F1event_a50 |          0  (omitted)
      F1event_b50 |          0  (omitted)
        ln_ew_ges |    3.31317   1.281609     2.59   0.010      .796325    5.830015
         ew_biodt |   .3912989    .029562    13.24   0.000     .3332447    .4493532
        ew_dtmihi |  -.2088727   .0600436    -3.48   0.001    -.3267874   -.0909581
         ew_ledig |   .2107352   .0806477     2.61   0.009      .052358    .3691124
       ew_married |   .2123578   .0802834     2.65   0.008      .054696    .3700196
        wb_anteil |  -.2423431   .0229985   -10.54   0.000    -.2875079   -.1971783
          wb_ausl |  -.0642884   .0143798    -4.47   0.000    -.0925278   -.0360491
         wb_18t24 |  -.0271654   .0282956    -0.96   0.337    -.0827328    .0284019
         wb_25t34 |    .036237    .018739     1.93   0.054     -.000563    .0730371
         wb_35t44 |  -.0095472   .0247997    -0.38   0.700    -.0582493    .0391549
         wb_45t59 |  -.0314301   .0209722    -1.50   0.134    -.0726157    .0097555
          avg_dur |   .0396344   .0234854     1.69   0.092    -.0064867    .0857555
          hh_kids |  -.0838038   .0412472    -2.03   0.043    -.1648058   -.0028018
mpreis_flats_rent |  -.0165942   .0237509    -0.70   0.485    -.0632366    .0300483
            _cons |  -18.64962   10.85332    -1.72   0.086    -39.96355    2.664319
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 8 iterations)
note: F1event_a50 omitted because of collinearity
note: F1event_b50 omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,527
Absorbing 2 HDFE groups                           F(  42,    617) =      29.31
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9905
                                                  Adj R-squared   =     0.9883
                                                  Within R-sq.    =     0.4379
Number of clusters (sb_new)  =        618         Root MSE        =     1.6202

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
      F7event_a50 |  -.6135827   .4411995    -1.39   0.165    -1.480017     .252852
      F6event_a50 |  -.3264046    .351972    -0.93   0.354    -1.017613    .3648037
      F5event_a50 |  -.5147779   .4312767    -1.19   0.233    -1.361726    .3321702
      F4event_a50 |  -.4459017   .2023039    -2.20   0.028    -.8431894    -.048614
      F3event_a50 |  -.1919214   .1821645    -1.05   0.292    -.5496591    .1658163
      F2event_a50 |  -.1661926   .1895365    -0.88   0.381    -.5384075    .2060223
      L0event_a50 |  -.0108731   .2366426    -0.05   0.963    -.4755958    .4538495
      L1event_a50 |   .1042545   .2675452     0.39   0.697    -.4211552    .6296642
      L2event_a50 |   .6665355   .2987791     2.23   0.026     .0797883    1.253283
      L3event_a50 |   .4487552   .2999354     1.50   0.135    -.1402628    1.037773
      L4event_a50 |   .4742287   .8620422     0.55   0.582    -1.218664    2.167121
      L5event_a50 |   .9008251   1.214687     0.74   0.459    -1.484597    3.286247
      L6event_a50 |  -.2380232   1.162831    -0.20   0.838     -2.52161    2.045564
      L7event_a50 |   -.484035   .8708188    -0.56   0.579    -2.194163    1.226093
      F7event_b50 |   .2977839    .322034     0.92   0.355    -.3346317    .9301995
      F6event_b50 |   .4544788   .2991037     1.52   0.129    -.1329058    1.041864
      F5event_b50 |  -.0226945   .2685426    -0.08   0.933    -.5500627    .5046737
      F4event_b50 |  -.1292343   .1717055    -0.75   0.452    -.4664323    .2079636
      F3event_b50 |  -.2356641   .1717319    -1.37   0.170    -.5729139    .1015857
      F2event_b50 |  -.1226482   .1447104    -0.85   0.397    -.4068328    .1615364
      L0event_b50 |   -.849295   .1900915    -4.47   0.000      -1.2226   -.4759902
      L1event_b50 |  -.5358958   .2346047    -2.28   0.023    -.9966162   -.0751754
      L2event_b50 |  -.1654207   .2597099    -0.64   0.524    -.6754433    .3446018
      L3event_b50 |  -.1034483   .2925967    -0.35   0.724    -.6780544    .4711578
      L4event_b50 |   .5443394   .4540121     1.20   0.231    -.3472569    1.435936
      L5event_b50 |   .3216712   .5704007     0.56   0.573     -.798491    1.441833
      L6event_b50 |   .4246946   .8642445     0.49   0.623    -1.272523    2.121912
      L7event_b50 |    .505515   .7794683     0.65   0.517    -1.025218    2.036248
      F1event_a50 |          0  (omitted)
      F1event_b50 |          0  (omitted)
        ln_ew_ges |   1.876059   1.181321     1.59   0.113    -.4438378    4.195956
         ew_biodt |   .7702287    .032659    23.58   0.000     .7060923    .8343651
        ew_dtmihi |  -.1639949    .053445    -3.07   0.002     -.268951   -.0590388
         ew_ledig |   .3960839   .0728752     5.44   0.000     .2529704    .5391974
       ew_married |   .6093329   .0711132     8.57   0.000     .4696796    .7489863
        wb_anteil |  -.5333246   .0253247   -21.06   0.000    -.5830577   -.4835915
          wb_ausl |  -.0517496   .0177898    -2.91   0.004    -.0866855   -.0168137
         wb_18t24 |  -.0528799   .0270815    -1.95   0.051     -.106063    .0003033
         wb_25t34 |  -.0144983   .0174633    -0.83   0.407    -.0487929    .0197963
         wb_35t44 |  -.0045439   .0215802    -0.21   0.833    -.0469234    .0378355
         wb_45t59 |  -.0194424   .0202812    -0.96   0.338     -.059271    .0203863
          avg_dur |   .0183804   .0232836     0.79   0.430    -.0273444    .0641052
          hh_kids |  -.1068165   .0373025    -2.86   0.004    -.1800719   -.0335612
mpreis_flats_rent |   .0072547   .0243101     0.30   0.765    -.0404859    .0549954
            _cons |  -.0570709   10.76119    -0.01   0.996    -21.19007    21.07593
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         // Rescaled dummies
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.          reghdfe `v' F7event_aX-L7event_aX  F7event_bX-L7event_bX F1event_aX F1event_bX $ctr $wg
> t ///
>                                          , absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new)
  3. 
.                 estimates store `v'_a_rs
  4.                 estimates store `v'_b_rs        
  5.         }               
(MWFE estimator converged in 5 iterations)
note: F1event_aX omitted because of collinearity
note: F1event_bX omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  42,    617) =      14.79
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9726
                                                  Adj R-squared   =     0.9669
                                                  Within R-sq.    =     0.2126
Number of clusters (sb_new)  =        618         Root MSE        =     1.6748

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_aX |    .181385    .514126     0.35   0.724    -.8282641    1.191034
       F6event_aX |  -.0554672   .4768424    -0.12   0.907     -.991898    .8809635
       F5event_aX |   .4840897    .366753     1.32   0.187    -.2361459    1.204325
       F4event_aX |    .011697    .239422     0.05   0.961    -.4584838    .4818777
       F3event_aX |  -.1130059    .240571    -0.47   0.639    -.5854432    .3594314
       F2event_aX |   -.076555   .1818269    -0.42   0.674    -.4336296    .2805195
       L0event_aX |   .3877151   .3121498     1.24   0.215    -.2252897     1.00072
       L1event_aX |   .1154103   .2941086     0.39   0.695     -.462165    .6929856
       L2event_aX |   .0152184    .291618     0.05   0.958    -.5574658    .5879026
       L3event_aX |   .2898293   .2965477     0.98   0.329    -.2925358    .8721944
       L4event_aX |    .165414   .6851228     0.24   0.809    -1.180041    1.510869
       L5event_aX |   .2361336   .6553794     0.36   0.719    -1.050911    1.523178
       L6event_aX |   1.188695   .6547755     1.82   0.070    -.0971639    2.474554
       L7event_aX |   1.693667   .8684453     1.95   0.052    -.0117997    3.399134
       F7event_bX |  -.7032079   .3680655    -1.91   0.057    -1.426021    .0196051
       F6event_bX |  -.2823387   .3392864    -0.83   0.406    -.9486349    .3839576
       F5event_bX |  -.0260781   .2853096    -0.09   0.927    -.5863738    .5342176
       F4event_bX |  -.0066632   .2031484    -0.03   0.974    -.4056093    .3922828
       F3event_bX |   .0152774   .1993346     0.08   0.939    -.3761792    .4067339
       F2event_bX |   .1648818   .1378504     1.20   0.232    -.1058311    .4355947
       L0event_bX |  -2.039652   .2368857    -8.61   0.000    -2.504852   -1.574452
       L1event_bX |  -1.781729   .2283683    -7.80   0.000    -2.230203   -1.333256
       L2event_bX |  -1.323641   .2517621    -5.26   0.000    -1.818056   -.8292271
       L3event_bX |  -.8568693     .26019    -3.29   0.001    -1.367835    -.345904
       L4event_bX |  -.6973653   .6194467    -1.13   0.261    -1.913845    .5191142
       L5event_bX |  -.3156291   .6994715    -0.45   0.652    -1.689263    1.058004
       L6event_bX |  -.1507235   .9490022    -0.16   0.874    -2.014389    1.712942
       L7event_bX |  -.3248935   .9221729    -0.35   0.725    -2.135872    1.486085
       F1event_aX |          0  (omitted)
       F1event_bX |          0  (omitted)
        ln_ew_ges |  -1.044801    .855438    -1.22   0.222    -2.724724     .635122
         ew_biodt |   .3756415   .0264225    14.22   0.000     .3237527    .4275304
        ew_dtmihi |   .0519077   .0470585     1.10   0.270    -.0405065     .144322
         ew_ledig |   .1769029   .0511924     3.46   0.001     .0763703    .2774354
       ew_married |   .3700155   .0528986     6.99   0.000     .2661325    .4738986
        wb_anteil |  -.2880872   .0195345   -14.75   0.000    -.3264493   -.2497251
          wb_ausl |   .0171631   .0150412     1.14   0.254     -.012375    .0467012
         wb_18t24 |  -.0271861   .0290924    -0.93   0.350    -.0843183     .029946
         wb_25t34 |  -.0611021   .0186636    -3.27   0.001     -.097754   -.0244503
         wb_35t44 |   .0132835    .021784     0.61   0.542    -.0294963    .0560634
         wb_45t59 |   .0100172   .0206788     0.48   0.628    -.0305922    .0506266
          avg_dur |  -.0098741   .0202403    -0.49   0.626    -.0496223    .0298742
          hh_kids |  -.0320051   .0408986    -0.78   0.434    -.1123223    .0483122
mpreis_flats_rent |    .033348   .0222461     1.50   0.134    -.0103393    .0770352
            _cons |   16.32374   8.113008     2.01   0.045     .3912871     32.2562
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)
note: F1event_aX omitted because of collinearity
note: F1event_bX omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  42,    617) =      16.92
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9610
                                                  Adj R-squared   =     0.9528
                                                  Within R-sq.    =     0.2336
Number of clusters (sb_new)  =        618         Root MSE        =     1.6766

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_aX |   -.492397   .4857629    -1.01   0.311    -1.446346     .461552
       F6event_aX |  -.0456964   .4390723    -0.10   0.917    -.9079537     .816561
       F5event_aX |  -.8180593   .3769381    -2.17   0.030    -1.558297    -.077822
       F4event_aX |  -.2420968   .2111592    -1.15   0.252    -.6567747    .1725812
       F3event_aX |   .1476601   .2110873     0.70   0.484    -.2668766    .5621968
       F2event_aX |   .0045579   .1715448     0.03   0.979    -.3323245    .3414403
       L0event_aX |  -.3388383   .2757783    -1.23   0.220    -.8804162    .2027395
       L1event_aX |   -.061766   .2813005    -0.22   0.826    -.6141886    .4906566
       L2event_aX |   .4041389   .3049411     1.33   0.186    -.1947094    1.002987
       L3event_aX |  -.1333821   .2880213    -0.46   0.643     -.699003    .4322387
       L4event_aX |  -.3430814   .9032979    -0.38   0.704    -2.116992     1.43083
       L5event_aX |   .3681454   .7970857     0.46   0.644    -1.197184    1.933475
       L6event_aX |  -1.784097   .7776334    -2.29   0.022    -3.311226   -.2569679
       L7event_aX |  -1.713184   .5812737    -2.95   0.003    -2.854699   -.5716694
       F7event_bX |   .8803751   .3203081     2.75   0.006     .2513489    1.509401
       F6event_bX |   .6452425   .2764916     2.33   0.020     .1022637    1.188221
       F5event_bX |   .1495913   .2965665     0.50   0.614    -.4328108    .7319934
       F4event_bX |  -.0312056   .1830654    -0.17   0.865    -.3907124    .3283013
       F3event_bX |  -.0911967   .1700231    -0.54   0.592    -.4250908    .2426974
       F2event_bX |  -.1091755   .1372447    -0.80   0.427    -.3786988    .1603478
       L0event_bX |   1.284964   .2343635     5.48   0.000     .8247171    1.745211
       L1event_bX |    1.59704    .226933     7.04   0.000     1.151386    2.042695
       L2event_bX |   1.449172   .2627873     5.51   0.000     .9331063    1.965238
       L3event_bX |   1.109897   .2852967     3.89   0.000     .5496264    1.670167
       L4event_bX |    2.24358   .6163352     3.64   0.000      1.03321    3.453949
       L5event_bX |   2.051738   .6433628     3.19   0.001     .7882912    3.315184
       L6event_bX |   2.042736   .8599655     2.38   0.018     .3539217     3.73155
       L7event_bX |   2.327554   .7623554     3.05   0.002     .8304282     3.82468
       F1event_aX |          0  (omitted)
       F1event_bX |          0  (omitted)
        ln_ew_ges |   2.456616   1.009705     2.43   0.015     .4737413     4.43949
         ew_biodt |   .3944425     .02719    14.51   0.000     .3410463    .4478387
        ew_dtmihi |  -.2128208   .0548664    -3.88   0.000    -.3205683   -.1050732
         ew_ledig |   .2441586   .0594749     4.11   0.000     .1273608    .3609563
       ew_married |   .2848383   .0606442     4.70   0.000     .1657442    .4039324
        wb_anteil |  -.2556187   .0219314   -11.66   0.000    -.2986878   -.2125495
          wb_ausl |  -.0762245   .0145889    -5.22   0.000    -.1048744   -.0475745
         wb_18t24 |  -.0150388   .0262871    -0.57   0.567    -.0666618    .0365842
         wb_25t34 |   .0467195   .0177229     2.64   0.009      .011915     .081524
         wb_35t44 |  -.0167432   .0233485    -0.72   0.474    -.0625953    .0291089
         wb_45t59 |  -.0412369   .0192561    -2.14   0.033    -.0790523   -.0034215
          avg_dur |   .0320539   .0214874     1.49   0.136    -.0101434    .0742512
          hh_kids |  -.0859615   .0372379    -2.31   0.021    -.1590898   -.0128331
mpreis_flats_rent |   -.013553   .0213616    -0.63   0.526    -.0555033    .0283972
            _cons |  -14.82852   8.786971    -1.69   0.092    -32.08451    2.427481
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)
note: F1event_aX omitted because of collinearity
note: F1event_bX omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  42,    617) =      33.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9899
                                                  Adj R-squared   =     0.9877
                                                  Within R-sq.    =     0.4534
Number of clusters (sb_new)  =        618         Root MSE        =     1.6391

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
       F7event_aX |  -.3110133   .4798284    -0.65   0.517    -1.253308    .6312816
       F6event_aX |  -.1011641   .3913794    -0.26   0.796    -.8697613    .6674331
       F5event_aX |  -.3339693   .4205409    -0.79   0.427    -1.159834    .4918958
       F4event_aX |  -.2303998   .2394893    -0.96   0.336    -.7007128    .2399132
       F3event_aX |   .0346536   .2063115     0.17   0.867    -.3705043    .4398115
       F2event_aX |  -.0719968   .1941743    -0.37   0.711    -.4533196    .3093259
       L0event_aX |   .0488763    .228627     0.21   0.831     -.400105    .4978577
       L1event_aX |   .0536443   .2890503     0.19   0.853    -.5139974    .6212859
       L2event_aX |   .4193572   .2818456     1.49   0.137    -.1341358    .9728503
       L3event_aX |   .1564473   .2898716     0.54   0.590    -.4128073    .7257018
       L4event_aX |  -.1776666   .7309313    -0.24   0.808    -1.613081    1.257748
       L5event_aX |   .6042776   .8513539     0.71   0.478    -1.067625     2.27618
       L6event_aX |  -.5954011   .9506985    -0.63   0.531    -2.462398    1.271596
       L7event_aX |   -.019516   .7238186    -0.03   0.978    -1.440963    1.401931
       F7event_bX |   .1771683   .3465561     0.51   0.609    -.5034042    .8577408
       F6event_bX |   .3629035   .3079039     1.18   0.239    -.2417632    .9675702
       F5event_bX |   .1235137   .2808418     0.44   0.660     -.428008    .6750354
       F4event_bX |  -.0378681    .186853    -0.20   0.839     -.404813    .3290769
       F3event_bX |  -.0759194   .1849094    -0.41   0.682    -.4390475    .2872087
       F2event_bX |   .0557068   .1508142     0.37   0.712    -.2404645    .3518781
       L0event_bX |  -.7546873   .1881621    -4.01   0.000    -1.124203   -.3851715
       L1event_bX |  -.1846883   .2156524    -0.86   0.392    -.6081901    .2388135
       L2event_bX |   .1255311   .2407394     0.52   0.602    -.3472369    .5982991
       L3event_bX |   .2530278   .2702676     0.94   0.350    -.2777281    .7837837
       L4event_bX |   1.546214   .4684706     3.30   0.001     .6262236    2.466204
       L5event_bX |   1.736109   .4553986     3.81   0.000     .8417897    2.630428
       L6event_bX |   1.892014   .6854861     2.76   0.006     .5458447    3.238182
       L7event_bX |   2.002663   .7677705     2.61   0.009     .4949024    3.510423
       F1event_aX |          0  (omitted)
       F1event_bX |          0  (omitted)
        ln_ew_ges |   1.411815   .9681685     1.46   0.145    -.4894902     3.31312
         ew_biodt |    .770084   .0305848    25.18   0.000     .7100211    .8301469
        ew_dtmihi |  -.1609129   .0502556    -3.20   0.001    -.2596056   -.0622201
         ew_ledig |   .4210616    .061924     6.80   0.000     .2994542    .5426689
       ew_married |   .6548539   .0598492    10.94   0.000     .5373211    .7723867
        wb_anteil |  -.5437059   .0241662   -22.50   0.000    -.5911638    -.496248
          wb_ausl |  -.0590614   .0178354    -3.31   0.001    -.0940868    -.024036
         wb_18t24 |  -.0422249   .0258352    -1.63   0.103    -.0929605    .0085107
         wb_25t34 |  -.0143826   .0165706    -0.87   0.386    -.0469242     .018159
         wb_35t44 |  -.0034596    .020196    -0.17   0.864    -.0431208    .0362016
         wb_45t59 |  -.0312197   .0191958    -1.63   0.104    -.0689167    .0064772
          avg_dur |   .0221798   .0211659     1.05   0.295    -.0193862    .0637458
          hh_kids |  -.1179666   .0343368    -3.44   0.001    -.1853977   -.0505355
mpreis_flats_rent |   .0197949   .0220339     0.90   0.369    -.0234757    .0630655
            _cons |   1.495218   8.907588     0.17   0.867    -15.99765    18.98808
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         // dCdH (2023):  CONT TREATMENT
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                 // dist increase
.                 did_multiplegt_dyn `v' sb_new wahl_id  treat_simple_clean if del_street_dist_Kma
> x>=0,  ///
>                         placebo(3) effects(3)  cluster(sb_new)  controls($ctr) weight(wahlber_ge
> samt)   graph_off
  3.                 // storing the estimates
.                 matrix dcdh_b_up`v' = e(estimates) 
  4.                 matrix dcdh_v_up`v' = e(variances)      
  5.                 matrix  rownames dcdh_b_up`v' =Effect_0 Effect_1 Effect_2 Av_tot_eff Placebo_
> 2 Placebo_3 Placebo_4 Placebo_1
  6.                 matrix  rownames dcdh_v_up`v' =Effect_0 Effect_1 Effect_2 Av_tot_eff Placebo_
> 2 Placebo_3 Placebo_4 Placebo_1    
  7.                 
.                 // dist decrease
.                 did_multiplegt_dyn `v' sb_new wahl_id   treat_simple_clean if del_street_dist_Km
> ax<=0, ///
>                         placebo(3) effects(3)  cluster(sb_new)  controls($ctr) weight(wahlber_ge
> samt)   graph_off       
  8.                 // storing the estimates
.                 matrix dcdh_b_dn`v' = e(estimates) 
  9.                 matrix dcdh_v_dn`v' = e(variances)      
 10.                 matrix  rownames dcdh_b_dn`v' =Effect_0 Effect_1 Effect_2 Av_tot_eff Placebo_
> 2 Placebo_3 Placebo_4 Placebo_1
 11.                 matrix  rownames dcdh_v_dn`v' =Effect_0 Effect_1 Effect_2 Av_tot_eff Placebo_
> 2 Placebo_3 Placebo_4 Placebo_1    
 12.         }       


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -1.504592   .2415494  -1.978029  -1.031155    2293297   473707.5 
    Effect_2 |  -1.82144   .2820096  -2.374179  -1.268701    1684618   312428.5 
    Effect_3 | -1.311776   .2767209  -1.854149  -.7694031    1129090   307885.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff | -1.928406   .2868107  -2.490555  -1.366257    3511147    1094022            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  .2904948   .1465789   .0032002   .5777895    2293297   473707.5 
   Placebo_2 |  .0008575    .265433  -.5193912   .5211063    1079207   304553.9 
   Placebo_3 | -.1350836   .2631424  -.6508427   .3806754   776507.3   300341.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .10738386


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 |  .2060738   .3024097  -.3866493   .7987968    1706384   301997.5 
    Effect_2 | -.2277237   .3388358  -.8918418   .4363945    1292006     211962 
    Effect_3 | -.3882079   .3115896  -.9989234   .2225076   872624.8   209942.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  -.126339   .3828807  -.8767852   .6241071    2560411   723902.3            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 | -.1931508   .1803844  -.5467042   .1604026    1706384   301997.5 
   Placebo_2 | -.3141471   .3366014  -.9738859   .3455917   850930.6   203009.3 
   Placebo_3 | -.3877384   .3397585  -1.053665   .2781884   616946.2   201086.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .6283748


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 |  .9454965   .2412036   .4727374   1.418256    2293297   473707.5 
    Effect_2 |  1.605536    .290751   1.035664   2.175408    1684618   312428.5 
    Effect_3 |   1.29333   .3091064   .6874811   1.899178    1129090   307885.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  1.541758   .3082285   .9376301   2.145886    3511147    1094022            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 | -.2525169   .1447683  -.5362626   .0312289    2293297   473707.5 
   Placebo_2 | -.3331906   .2206986  -.7657598   .0993786    1079207   304553.9 
   Placebo_3 | -.2647955   .1798221  -.6172468   .0876558   776507.3   300341.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .1296892


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -.1227922   .2848207  -.6810408   .4354565    1706384   301997.5 
    Effect_2 | -.0455195   .3434746  -.7187297   .6276906    1292006     211962 
    Effect_3 |  .4999889   .3740915  -.2332303   1.233208   872624.8   209942.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |   .108944    .405644  -.6861182   .9040061    2560411   723902.3            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  .1530588   .1550289  -.1507979   .4569155    1706384   301997.5 
   Placebo_2 | -.0141273   .2703119  -.5439387    .515684   850930.6   203009.3 
   Placebo_3 | -.1054854   .2353343  -.5667406   .3557698   616946.2   201086.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .60475452


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -.5590957    .230134  -1.010158  -.1080332    2293297   473707.5 
    Effect_2 | -.2159033   .2958734  -.7958151   .3640085    1684618   312428.5 
    Effect_3 | -.0184459   .3315296   -.668244   .6313522    1129090   307885.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff | -.3866475   .3028635  -.9802599   .2069649    3511147    1094022            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  .0379781   .1749028  -.3048314   .3807877    2293297   473707.5 
   Placebo_2 | -.3323324    .230392  -.7839007    .119236    1079207   304553.9 
   Placebo_3 | -.3998786   .2344618  -.8594237   .0596666   776507.3   300341.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .26779792


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 |  .0832815   .2788123  -.4631906   .6297535    1706384   301997.5 
    Effect_2 | -.2732432   .3565091   -.972001   .4255146    1292006     211962 
    Effect_3 |  .1117814   .3852905   -.643388   .8669508   872624.8   209942.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff | -.0173948   .3989064  -.7992514   .7644618    2560411   723902.3            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  -.040092   .2003335  -.4327457   .3525617    1706384   301997.5 
   Placebo_2 | -.3282752   .3086774   -.933283   .2767325   850930.6   203009.3 
   Placebo_3 | -.4932235   .3060125  -1.093008    .106561   616946.2   201086.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .35324864


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).

.                         
.         
. ** PLOT         
.         // legend
.         local legd `"order(1 "Binary treatment: full reassignments (baseline)" 3 "Binary treatme
> nt: 50%+ addresses reassigned" 5 "Continuous treatment: rescaled event-time dummies" 7 "Continuo
> us treatment (deChaisemartin et al , 2023)" )"' 

.         
.  foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                 
.                 // PLOT Dist Decreas
.                 event_plot  `v'_a_bsl `v'_a_50 `v'_a_rs dcdh_b_dn`v'#dcdh_v_dn`v', ///
>                 stub_lag(L#event_a L#event_a50 L#event_aX Effect_#) stub_lead(F#event_a F#event_
> a50 F#event_aX Placebo_#) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(small)) xlabel(-4(1)2) xtitle("
> Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `legd' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 subtitle("{bf:b.} Distance decrease",nobox justification(left) size(medsmall)) /
> //
>                 name(`v'_dwn, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry))  /
> //
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green)) 
  3.         
.                 // PLOT Dist Increase
.                 event_plot  `v'_b_bsl `v'_b_50 `v'_b_rs dcdh_b_up`v'#dcdh_v_up`v' , ///
>                 stub_lag(L#event_b L#event_b50 L#event_bX Effect_#) stub_lead(F#event_b F#event_
> b50 F#event_bX Placebo_#) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.24(0.16)0.24) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(ytitle("Voter turnout in %""(estimates)", size(small)) xlabel(-4(1)2) xtitle("
> Election since reassignment", size(medsmall)) ///
>                 legend(pos(12) `legd' row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 subtitle("{bf:a.} Distance increase",nobox justification(left) size(medsmall)) /
> //
>                 name(`v'_up, replace) ) ///
>         lag_opt1(msymbol(S) msize(3pt) color(teal))  lag_ci_opt1(color(teal)) ///
>         lag_opt2(msymbol(+) msize(3pt) color(black))    lag_ci_opt2(color(black)) ///
>         lag_opt3(msymbol(Sh) msize(3pt) color(cranberry))       lag_ci_opt3(color(cranberry))  /
> //
>         lag_opt4(msymbol(Oh) msize(3pt) color(midblue))         lag_ci_opt4(color(midblue))  ///
>         lag_opt5(msymbol(Th) msize(3pt) color(forest_green)) lag_ci_opt5(color(forest_green))   
  4.         
.         
.  }      

.  
.         * PLOT: FIGURE C9. Robustness to Alternative Treatment Definitions–Effects by Distance C
> hange
.         grc1leg2 turnout_urne_up turnout_urne_dwn,               name(g1, replace) ///
>                         title("{bf:Panel A.} Effect on Polling Place Turnout", just(left) bexpan
> d size(small)) loff  iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 turnout_pos_req_up turnout_pos_req_dwn, name(g2, replace) ///
>                         title("{bf:Panel B.} Effect on Mail-in Turnout", just(left) bexpand size
> (small)) loff  iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 turnout_tot_req_up turnout_tot_req_dwn, name(g3, replace) ///
>                         title("{bf:Panel C.} Effect on Total Turnout", just(left) bexpand size(s
> mall))   iscale(.7) imargins(small)
-grc1leg2- working...

.         grc1leg2 g1 g2 g3, col(1) imargins(zero) legscale(*.65) lrow(2)
-grc1leg2- working...

Note: To preserve in a saved graphics file the legend's row and column rearrangements
made with the options -lrows()- and/or -lcols()-, specify the suboption -asis-
for the saving() option or for the graph save command.


.         gr_edit .style.editstyle declared_ysize(6) editcopy     

.         graph export "$figures/Figure_C9_ES_alternative_treat_dist2.pdf", replace               
>                 
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C9_ES_
    > alternative_treat_dist2.pdf saved as PDF format

.         
.         
. ********************************************************************************
. // deChaisemartin et al, 2023 estimator: cont treatment based on dist change (Fig C11) //
. ********************************************************************************                
.         
. forvalues x=1/3 {
  2.         
.         if `x'==1 {
  3.                 loc sw ""
  4.                 loc tt "{bf: Panel A.} Pooled Sample"
  5.         }
  6.         if `x'==2 {
  7.                 loc sw "switchers(out)"
  8.                 loc tt "{bf: Panel B.} Distance Decreases ('switchers out')"
  9.         }       
 10.         if `x'==3 {
 11.                 loc sw "switchers(in)"
 12.                 loc tt "{bf: Panel C.} Distance Increases ('switchers in')"             
 13.         }       
 14. 
.         // dCdH (2023):  CONT TREATMENT
.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
 15.         
.                 did_multiplegt_dyn `v' sb_new wahl_id  del_street_dist_cum,  placebo(3) effects(
> 3)  cluster(sb_new)  ///
>                         controls($ctr  dctf* ) weight(wahlber_gesamt) graph_off drop_larger_lowe
> r `sw' 
 16. 
.                 // storing the estimates
.                 matrix dcdh_b_`v'`x' = e(estimates) 
 17.                 matrix dcdh_v_`v'`x' = e(variances)     
 18.                 matrix  rownames dcdh_b_`v'`x' =Effect_0 Effect_1 Effect_2 Av_tot_eff Placebo
> _2 Placebo_3 Placebo_4 Placebo_1
 19.                 matrix  rownames dcdh_v_`v'`x' =Effect_0 Effect_1 Effect_2 Av_tot_eff Placebo
> _2 Placebo_3 Placebo_4 Placebo_1   
 20.         }
 21. }               


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -1.081564   .2548569  -1.581083  -.5820443    2585841   682836.3 
    Effect_2 | -1.173666   .2695536  -1.701991  -.6453406    2017115   527984.7 
    Effect_3 | -.9701726   .2553377  -1.470635  -.4697106    1216885   503730.6 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff | -5.650767   1.205537   -8.01362  -3.287914    4409368    1714552            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  .0536842   .1459099  -.2322991   .3396675    2585841   682836.3 
   Placebo_2 |  .0113321   .2719383   -.521667   .5443312    1199255   501936.4 
   Placebo_3 |  .0657363   .2656024  -.4548445   .5863171   918593.2   481477.6 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .96327803


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 |  .5751586   .2406319   .1035201   1.046797    2585841   682836.3 
    Effect_2 |  .8362103   .2895394    .268713   1.403707    2017115   527984.7 
    Effect_3 |  .7038215   .3042549    .107482   1.300161    1216885   503730.6 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  3.637164   1.317006   1.055833   6.218496    4409368    1714552            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 | -.1146723   .1288945  -.3673054   .1379609    2585841   682836.3 
   Placebo_2 | -.1500964   .2143777  -.5702768   .2700839    1199255   501936.4 
   Placebo_3 |  -.162094   .1695929  -.4944961   .1703081   918593.2   481477.6 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .65772965


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -.5064053   .2378417  -.9725751  -.0402355    2585841   682836.3 
    Effect_2 | -.3374549   .3050937  -.9354385   .2605286    2017115   527984.7 
    Effect_3 | -.2663509   .3272897  -.9078386   .3751368    1216885   503730.6 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff | -2.013603   1.340097  -4.640192   .6129862    4409368    1714552            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 | -.0609881    .196639  -.4464005   .3244243    2585841   682836.3 
   Placebo_2 | -.1387639   .2617053  -.6517063   .3741785    1199255   501936.4 
   Placebo_3 | -.0963572   .2420032  -.5706834    .377969   918593.2   481477.6 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .96151198


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 |  .1070484   .3297236  -.5392098   .7533066    2178294   275288.7 
    Effect_2 |  .2096527   .3607884  -.4974926    .916798    1699275   210144.3 
    Effect_3 |  .3142962   .3631322  -.3974428   1.026035   910047.1   196892.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  1.276388   1.973781  -2.592223   5.144999    3377142   682325.9            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  -.041521   .2303642  -.4930347   .4099928    2178294   275288.7 
   Placebo_2 |  .4112376   .4081737  -.3887829   1.211258   893819.1   196500.5 
   Placebo_3 |  .4703188   .3486089  -.2129546   1.153592   622256.5   185140.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .35643662


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -.1634568   .3164993  -.7837954   .4568818    2178294   275288.7 
    Effect_2 |  .0637813   .3562131  -.6343965    .761959    1699275   210144.3 
    Effect_3 | -.3656057   .3824407  -1.115189   .3839782   910047.1   196892.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  -.976357   2.035645   -4.96622   3.013506    3377142   682325.9            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |   .093326   .1519717  -.2045387   .3911906    2178294   275288.7 
   Placebo_2 |  .0496654   .2875225  -.5138787   .6132095   893819.1   196500.5 
   Placebo_3 |   .146263   .2286397  -.3018708   .5943968   622256.5   185140.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .83324656


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -.0564087   .2991267  -.6426971   .5298797    2178294   275288.7 
    Effect_2 |  .2734341   .3823579  -.4759873   1.022856    1699275   210144.3 
    Effect_3 | -.0513102   .4211085  -.8766829   .7740625   910047.1   196892.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  .3000284   2.054749   -3.72728   4.327337    3377142   682325.9            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |  .0518047   .2925856  -.5216631   .6252725    2178294   275288.7 
   Placebo_2 |  .4609045   .4222729  -.3667504   1.288559   893819.1   196500.5 
   Placebo_3 |  .6165823   .3151022  -.0010181   1.234183   622256.5   185140.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .15506608


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -1.884443   .2873969  -2.447741  -1.321145    2310552   407547.6 
    Effect_2 | -2.088265   .3103868  -2.696623  -1.479907    1806971   317840.3 
    Effect_3 | -1.794396   .2910987  -2.364949  -1.223842    1019992   306837.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff | -8.979738   1.221991  -11.37484  -6.584636    3727042    1032226            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 |   .117993   .1480874  -.1722583   .4082443    2310552   407547.6 
   Placebo_2 | -.2459449   .2772537  -.7893621   .2974724    1002755   305435.9 
   Placebo_3 | -.1870327   .2860022   -.747597   .3735317   733452.4   296336.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .45023993


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 |  1.074076   .2678856   .5490199   1.599131    2310552   407547.6 
    Effect_2 |  1.346912    .328073   .7038888   1.989935    1806971   317840.3 
    Effect_3 |  1.390056    .340601   .7224777   2.057634    1019992   306837.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  5.854276   1.317116   3.272728   8.435823    3727042    1032226            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 | -.2551701   .1511292  -.5513834   .0410432    2310552   407547.6 
   Placebo_2 | -.2786121   .2365181  -.7421876   .1849635    1002755   305435.9 
   Placebo_3 | -.3547446   .1825901  -.7126213    .003132   733452.4   296336.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .09797635


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).


--------------------------------------------------------------------------------
             Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_1 | -.8103673   .2616703  -1.323241  -.2974935    2310552   407547.6 
    Effect_2 | -.7413525   .3428102   -1.41326  -.0694445    1806971   317840.3 
    Effect_3 | -.4043391   .3667783  -1.123225   .3145464    1019992   306837.8 
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
    Estimation of treatment effects: Average total effect per treatment unit
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N     Switch  x Periods 
-------------+-----------------------------------------------------------------------------
  Av_tot_eff |  -3.12546   1.327097   -5.72657  -.5243508    3727042    1032226            
--------------------------------------------------------------------------------


--------------------------------------------------------------------------------
          Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
   Placebo_1 | -.1371769   .2043692  -.5377406   .2633867    2310552   407547.6 
   Placebo_2 | -.5245572   .2656716  -1.045274  -.0038408    1002755   305435.9 
   Placebo_3 | -.5417768   .2617088  -1.054726  -.0288275   733452.4   296336.8 
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .17835004


The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°1010438
> 99).

. 
. 
.         // PLOT:  Overall Effects
.         event_plot  dcdh_b_turnout_urne1#dcdh_v_turnout_urne1 dcdh_b_turnout_pos_req1#dcdh_v_tur
> nout_pos_req1 dcdh_b_turnout_tot_req1#dcdh_v_turnout_tot_req1 , ///
>         stub_lag(Effect_#  ) stub_lead(Placebo_#  ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.15(0.15)0.15) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4
> (1)2) xtitle("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12)order(1 "Polling place turnout" 3 "Mail-in turnout" 5 "Total turno
> ut" ) row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf: Panel A.} Pooled Sample",nobox span bexpand justification(left) size
> (12pt)) ///
>                 name(dCdH_pool, replace)) ///
>         lag_opt1(msymbol(S) msize(3pt) color(navy))     lag_ci_opt1(color(navy)) ///
>         lag_opt2(msymbol(O) msize(3pt) color(cranberry))        lag_ci_opt2(color(cranberry)) //
> /  
>         lag_opt3(msymbol(Oh) msize(3pt) color(black))   lag_ci_opt3(color(black))               
>         

.         
.         
.         // PLOT: Switchers out (decreas)
.         event_plot dcdh_b_turnout_urne2#dcdh_v_turnout_urne2 dcdh_b_turnout_pos_req2#dcdh_v_turn
> out_pos_req2 dcdh_b_turnout_tot_req2#dcdh_v_turnout_tot_req2 , ///
>         stub_lag(Effect_#  ) stub_lead(Placebo_#  ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.15(0.15)0.15) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4
> (1)2) xtitle("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12)order(1 "Polling place turnout" 3 "Mail-in turnout" 5 "Total turno
> ut" ) row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf: Panel B.} Distance Decreases ('switchers out')"     ,nobox span bexp
> and just(left) size(12pt)) ///
>                 name(dCdH_dwn, replace)) ///
>         lag_opt1(msymbol(S) msize(3pt) color(navy))     lag_ci_opt1(color(navy)) ///
>         lag_opt2(msymbol(O) msize(3pt) color(cranberry))        lag_ci_opt2(color(cranberry)) //
> /  
>         lag_opt3(msymbol(Oh) msize(3pt) color(black))   lag_ci_opt3(color(black))

. 
.         
.         // PLOT: Switchers in (increase)
.         event_plot  dcdh_b_turnout_urne3#dcdh_v_turnout_urne3 dcdh_b_turnout_pos_req3#dcdh_v_tur
> nout_pos_req3 dcdh_b_turnout_tot_req3#dcdh_v_turnout_tot_req3 , ///
>         stub_lag(Effect_#  ) stub_lead(Placebo_#  ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.15(0.15)0.15) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4
> (1)2) xtitle("Election since reassignment", size(medsmall)) ///
>                 legend(pos(12)order(1 "Polling place turnout" 3 "Mail-in turnout" 5 "Total turno
> ut" ) row(1) region(style(none)) ) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf: Panel C.} Distance Increases ('switchers in')"      ,nobox span bexp
> and just(left) size(12pt)) ///
>                 name(dCdH_up, replace)) ///
>         lag_opt1(msymbol(S) msize(3pt) color(navy))     lag_ci_opt1(color(navy)) ///
>         lag_opt2(msymbol(O) msize(3pt) color(cranberry))        lag_ci_opt2(color(cranberry)) //
> /  
>         lag_opt3(msymbol(Oh) msize(3pt) color(black))   lag_ci_opt3(color(black))

.         
.         * PLOT: FIGURE C11. Robustness to Alternative Treatment Definition: Continuous Treatment
>  and de Chaisemartin et al. (2023) estimator
.         grc1leg2  dCdH_pool dCdH_dwn dCdH_up , xcommon  col(2) iscale(.7) ///
>         pos(4) ring(0) lcol(1) ltitle("Outcome:")  ///
>                                 lxoffset(-20) lyoffset(20) legscale(*.9) ltsize(*.8)
-grc1leg2- working...

Note: To preserve in a saved graphics file the legend's row and column rearrangements
made with the options -lrows()- and/or -lcols()-, specify the suboption -asis-
for the saving() option or for the graph save command.


.         gr_edit .legend.title.DragBy 0 -8

.         graph export "$figures/Figure_C11_ES_dCdH23_dist.pdf", replace  
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C11_ES
    > _dCdH23_dist.pdf saved as PDF format

. 
.         // report estimates
.         mat list  dcdh_b_turnout_urne3

dcdh_b_turnout_urne3[8,1]
                    c1
  Effect_0  -1.8844429
  Effect_1   -2.088265
  Effect_2  -1.7943955
Av_tot_eff  -8.9797378
 Placebo_2   .11799303
 Placebo_3  -.24594486
 Placebo_4  -.18703269
 Placebo_1           0

.         mat list  dcdh_b_turnout_pos_req3

dcdh_b_turnout_pos_req3[8,1]
                    c1
  Effect_0   1.0740757
  Effect_1   1.3469119
  Effect_2   1.3900557
Av_tot_eff   5.8542755
 Placebo_2  -.25517009
 Placebo_3  -.27861205
 Placebo_4  -.35474465
 Placebo_1           0

. 
. 
. 
. ********************************************************************************
.  // Triple diff, Event=largest reassignment, different dist slopes (Figure C10) //
. ********************************************************************************        
. 
.         
.         cap drop *X_n

.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_aX_n = F`l'eventX *ind_dist_dnX        *del_street_dist_max
>  //*del_street_dist_max    // a := decrease
  3.                 lab var F`l'event_aX_n "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_bX_n = F`l'eventX *ind_dist_upX        *del_street_
> dist_max //*del_street_dist_max    // b:= increase
  5.                 lab var F`l'event_bX_n "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_aX_n = L`l'eventX *ind_dist_dnX        *del_street_dist_max
>  //*del_street_dist_max // a := decrease
  3.                 lab var L`l'event_aX_n "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_bX_n = L`l'eventX *ind_dist_upX * del_street_dist_m
> ax //*del_street_dist_max  // b:= increase
  5.                 lab var L`l'event_bX_n "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.         }       

.         order F1event*, last

.         
.         // Estimate ES: base levels + interactions
.         estimates clear

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.         
.                 reghdfe `v' F7eventRS-L7eventRS F7event_aX_n-L7event_aX_n F7event_bX_n-L7event_b
> X_n F1eventRS F1event_aX_n F1event_bX_n ///
>                                 $ctr  $wgt, absorb(i.wahl_id#i.stadtbez i.sb_new) cluster(sb_new
> )
  3.                 estimates store `v'
  4.         }
(MWFE estimator converged in 5 iterations)
note: F7event_bX_n omitted because of collinearity
note: F6event_aX_n omitted because of collinearity
note: F6event_bX_n omitted because of collinearity
note: F5event_aX_n omitted because of collinearity
note: F5event_bX_n omitted because of collinearity
note: F4event_aX_n omitted because of collinearity
note: F4event_bX_n omitted because of collinearity
note: F3event_aX_n omitted because of collinearity
note: F3event_bX_n omitted because of collinearity
note: F2event_aX_n omitted because of collinearity
note: F2event_bX_n omitted because of collinearity
note: L0event_aX_n omitted because of collinearity
note: L0event_bX_n omitted because of collinearity
note: L1event_aX_n omitted because of collinearity
note: L1event_bX_n omitted because of collinearity
note: L2event_aX_n omitted because of collinearity
note: L2event_bX_n omitted because of collinearity
note: L3event_aX_n omitted because of collinearity
note: L3event_bX_n omitted because of collinearity
note: L4event_aX_n omitted because of collinearity
note: L4event_bX_n omitted because of collinearity
note: L5event_aX_n omitted because of collinearity
note: L5event_bX_n omitted because of collinearity
note: L6event_aX_n omitted because of collinearity
note: L6event_bX_n omitted because of collinearity
note: L7event_aX_n omitted because of collinearity
note: F1eventRS omitted because of collinearity
note: F1event_aX_n omitted because of collinearity
note: F1event_bX_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  56,    617) =      15.27
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9737
                                                  Adj R-squared   =     0.9680
                                                  Within R-sq.    =     0.2429
Number of clusters (sb_new)  =        618         Root MSE        =     1.6451

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7eventRS |  -.5901769   .4277732    -1.38   0.168    -1.430245    .2498912
        F6eventRS |  -.4867435   .4119668    -1.18   0.238    -1.295771    .3222836
        F5eventRS |   .0208527   .3321304     0.06   0.950    -.6313903    .6730958
        F4eventRS |  -.0895877   .2289704    -0.39   0.696    -.5392434     .360068
        F3eventRS |  -.2386176   .2166056    -1.10   0.271    -.6639911     .186756
        F2eventRS |    .122748   .1692472     0.73   0.469    -.2096225    .4551185
        L0eventRS |  -.7798676   .2532204    -3.08   0.002    -1.277146   -.2825893
        L1eventRS |   -.982333   .2630328    -3.73   0.000    -1.498881   -.4657848
        L2eventRS |  -.6738973   .2871845    -2.35   0.019    -1.237875   -.1099197
        L3eventRS |  -.4470761   .2722719    -1.64   0.101    -.9817681    .0876158
        L4eventRS |   .0452851    .644442     0.07   0.944     -1.22028    1.310851
        L5eventRS |  -.0621646   .6789983    -0.09   0.927    -1.395592    1.271263
        L6eventRS |   .7067986   1.089414     0.65   0.517     -1.43261    2.846207
        L7eventRS |   .6209321   1.085791     0.57   0.568    -1.511363    2.753227
     F7event_aX_n |    3.08619   1.949369     1.58   0.114    -.7420134    6.914393
     F7event_bX_n |  -.1952255   1.007689    -0.19   0.846    -2.174142    1.783691
     F6event_aX_n |     2.5535   1.914074     1.33   0.183     -1.20539    6.312391
     F6event_bX_n |   .2189863   .9644917     0.23   0.820    -1.675098    2.113071
     F5event_aX_n |    3.79784   1.355723     2.80   0.005     1.135448    6.460232
     F5event_bX_n |  -.6542434   .8603041    -0.76   0.447    -2.343723    1.035236
     F4event_aX_n |   1.310045   .8390656     1.56   0.119    -.3377261    2.957815
     F4event_bX_n |  -.1374523   .5581742    -0.25   0.806    -1.233604    .9586992
     F3event_aX_n |   .9289665   .6864932     1.35   0.176      -.41918    2.277113
     F3event_bX_n |   .4982046    .577259     0.86   0.388     -.635426    1.631835
     F2event_aX_n |  -.2888247   .6158899    -0.47   0.639    -1.498319    .9206699
     F2event_bX_n |  -.0688969   .4178669    -0.16   0.869    -.8895108    .7517169
     L0event_aX_n |   5.511469   1.057099     5.21   0.000     3.435521    7.587417
     L0event_bX_n |  -3.840779   .7257212    -5.29   0.000    -5.265962   -2.415596
     L1event_aX_n |   4.761155   .9898501     4.81   0.000     2.817272    6.705039
     L1event_bX_n |  -2.235497   .7189892    -3.11   0.002     -3.64746   -.8235347
     L2event_aX_n |   4.215064   1.072666     3.93   0.000     2.108545    6.321584
     L2event_bX_n |  -2.433756    .697916    -3.49   0.001    -3.804335   -1.063177
     L3event_aX_n |   4.008486   .8545793     4.69   0.000      2.33025    5.686723
     L3event_bX_n |  -1.622331   .6536066    -2.48   0.013    -2.905894   -.3387677
     L4event_aX_n |   3.405909   2.408301     1.41   0.158    -1.323551     8.13537
     L4event_bX_n |  -3.669595   2.662531    -1.38   0.169    -8.898317    1.559127
     L5event_aX_n |   2.932844   2.778244     1.06   0.292    -2.523117    8.388804
     L5event_bX_n |  -1.087437    2.27195    -0.48   0.632    -5.549128    3.374255
     L6event_aX_n |   4.737526   5.351885     0.89   0.376    -5.772593    15.24764
     L6event_bX_n |  -3.362997   3.995899    -0.84   0.400    -11.21021    4.484214
     L7event_aX_n |   5.999743   5.891412     1.02   0.309    -5.569907    17.56939
     F7event_bX_n |          0  (omitted)
     F6event_aX_n |          0  (omitted)
     F6event_bX_n |          0  (omitted)
     F5event_aX_n |          0  (omitted)
     F5event_bX_n |          0  (omitted)
     F4event_aX_n |          0  (omitted)
     F4event_bX_n |          0  (omitted)
     F3event_aX_n |          0  (omitted)
     F3event_bX_n |          0  (omitted)
     F2event_aX_n |          0  (omitted)
     F2event_bX_n |          0  (omitted)
     L0event_aX_n |          0  (omitted)
     L0event_bX_n |          0  (omitted)
     L1event_aX_n |          0  (omitted)
     L1event_bX_n |          0  (omitted)
     L2event_aX_n |          0  (omitted)
     L2event_bX_n |          0  (omitted)
     L3event_aX_n |          0  (omitted)
     L3event_bX_n |          0  (omitted)
     L4event_aX_n |          0  (omitted)
     L4event_bX_n |          0  (omitted)
     L5event_aX_n |          0  (omitted)
     L5event_bX_n |          0  (omitted)
     L6event_aX_n |          0  (omitted)
     L6event_bX_n |          0  (omitted)
     L7event_aX_n |          0  (omitted)
     L7event_bX_n |  -3.330361   2.837815    -1.17   0.241    -8.903309    2.242586
        F1eventRS |          0  (omitted)
     F1event_aX_n |          0  (omitted)
     F1event_bX_n |          0  (omitted)
        ln_ew_ges |  -.7425228   .8032545    -0.92   0.356    -2.319967    .8349214
         ew_biodt |   .3753296   .0254335    14.76   0.000     .3253828    .4252764
        ew_dtmihi |   .0312182   .0477571     0.65   0.514    -.0625681    .1250044
         ew_ledig |   .1805115   .0583441     3.09   0.002     .0659343    .2950887
       ew_married |   .3662089   .0584885     6.26   0.000     .2513483    .4810694
        wb_anteil |  -.2848574   .0187142   -15.22   0.000    -.3216086   -.2481061
          wb_ausl |   .0204319   .0141076     1.45   0.148    -.0072728    .0481366
         wb_18t24 |  -.0249826   .0289464    -0.86   0.388     -.081828    .0318627
         wb_25t34 |    -.05006    .018203    -2.75   0.006    -.0858074   -.0143126
         wb_35t44 |   .0039235   .0206376     0.19   0.849     -.036605    .0444521
         wb_45t59 |   .0102148   .0208011     0.49   0.624    -.0306348    .0510644
          avg_dur |  -.0178999   .0201238    -0.89   0.374    -.0574193    .0216196
          hh_kids |  -.0275254   .0394017    -0.70   0.485     -.104903    .0498522
mpreis_flats_rent |   .0392334   .0224101     1.75   0.080     -.004776    .0832428
            _cons |   13.90771   7.701189     1.81   0.071    -1.216013    29.03143
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)
note: F7event_bX_n omitted because of collinearity
note: F6event_aX_n omitted because of collinearity
note: F6event_bX_n omitted because of collinearity
note: F5event_aX_n omitted because of collinearity
note: F5event_bX_n omitted because of collinearity
note: F4event_aX_n omitted because of collinearity
note: F4event_bX_n omitted because of collinearity
note: F3event_aX_n omitted because of collinearity
note: F3event_bX_n omitted because of collinearity
note: F2event_aX_n omitted because of collinearity
note: F2event_bX_n omitted because of collinearity
note: L0event_aX_n omitted because of collinearity
note: L0event_bX_n omitted because of collinearity
note: L1event_aX_n omitted because of collinearity
note: L1event_bX_n omitted because of collinearity
note: L2event_aX_n omitted because of collinearity
note: L2event_bX_n omitted because of collinearity
note: L3event_aX_n omitted because of collinearity
note: L3event_bX_n omitted because of collinearity
note: L4event_aX_n omitted because of collinearity
note: L4event_bX_n omitted because of collinearity
note: L5event_aX_n omitted because of collinearity
note: L5event_bX_n omitted because of collinearity
note: L6event_aX_n omitted because of collinearity
note: L6event_bX_n omitted because of collinearity
note: L7event_aX_n omitted because of collinearity
note: F1eventRS omitted because of collinearity
note: F1event_aX_n omitted because of collinearity
note: F1event_bX_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  56,    617) =      15.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9622
                                                  Adj R-squared   =     0.9541
                                                  Within R-sq.    =     0.2563
Number of clusters (sb_new)  =        618         Root MSE        =     1.6545

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7eventRS |   .6418533   .4323704     1.48   0.138    -.2072427    1.490949
        F6eventRS |   .8401199   .3661269     2.29   0.022     .1211139    1.559126
        F5eventRS |  -.3188582   .3426878    -0.93   0.352    -.9918341    .3541177
        F4eventRS |  -.0894395    .218475    -0.41   0.682    -.5184842    .3396052
        F3eventRS |   .2203331   .2113862     1.04   0.298    -.1947905    .6354567
        F2eventRS |  -.0109509   .1536555    -0.07   0.943     -.312702    .2908002
        L0eventRS |   .3895163   .2468931     1.58   0.115    -.0953364     .874369
        L1eventRS |   .7705283   .2584501     2.98   0.003     .2629797    1.278077
        L2eventRS |   .8508742   .2985976     2.85   0.005     .2644833    1.437265
        L3eventRS |   .6923908   .3090047     2.24   0.025     .0855623    1.299219
        L4eventRS |    .858915   .7491265     1.15   0.252    -.6122318    2.330062
        L5eventRS |   1.679223     .67213     2.50   0.013     .3592829    2.999163
        L6eventRS |    1.62949   1.142978     1.43   0.154    -.6151089    3.874088
        L7eventRS |   1.549029   .9985256     1.55   0.121    -.4118922     3.50995
     F7event_aX_n |  -4.385061   1.617658    -2.71   0.007    -7.561843   -1.208278
     F7event_bX_n |   .3614233   .7614036     0.47   0.635    -1.133834     1.85668
     F6event_aX_n |   -3.58726   1.425812    -2.52   0.012    -6.387293   -.7872274
     F6event_bX_n |  -.5528622   .7442792    -0.74   0.458     -2.01449    .9087654
     F5event_aX_n |  -2.827969   1.173153    -2.41   0.016    -5.131826   -.5241108
     F5event_bX_n |   1.510695   .8876562     1.70   0.089    -.2324991    3.253888
     F4event_aX_n |  -.7957841   .7597933    -1.05   0.295    -2.287879    .6963102
     F4event_bX_n |   .2218823   .4844889     0.46   0.647     -.729565     1.17333
     F3event_aX_n |  -.3846274   .6614688    -0.58   0.561     -1.68363    .9143757
     F3event_bX_n |  -.7913547   .4670904    -1.69   0.091    -1.708634     .125925
     F2event_aX_n |   -.226571   .4148458    -0.55   0.585    -1.041252    .5881099
     F2event_bX_n |  -.1593241   .3966933    -0.40   0.688    -.9383569    .6197086
     L0event_aX_n |  -3.957866     .88299    -4.48   0.000    -5.691896   -2.223836
     L0event_bX_n |   2.915818    .663975     4.39   0.000     1.611893    4.219743
     L1event_aX_n |  -3.475977    .911245    -3.81   0.000    -5.265495   -1.686459
     L1event_bX_n |   2.336164   .7319354     3.19   0.001     .8987774    3.773551
     L2event_aX_n |  -3.031358   .9753476    -3.11   0.002    -4.946761   -1.115954
     L2event_bX_n |   2.207192   .8928018     2.47   0.014     .4538929     3.96049
     L3event_aX_n |  -3.588834   .7026147    -5.11   0.000     -4.96864   -2.209028
     L3event_bX_n |   1.298574    .799428     1.62   0.105    -.2713562    2.868503
     L4event_aX_n |  -4.770179   3.188957    -1.50   0.135     -11.0327    1.492346
     L4event_bX_n |   4.026308   2.317665     1.74   0.083     -.525161    8.577776
     L5event_aX_n |  -6.185761   2.399339    -2.58   0.010    -10.89762     -1.4739
     L5event_bX_n |   1.301459   2.007021     0.65   0.517    -2.639962    5.242879
     L6event_aX_n |   -15.5603   4.559743    -3.41   0.001     -24.5148   -6.605806
     L6event_bX_n |   .8920408   2.448977     0.36   0.716      -3.9173    5.701382
     L7event_aX_n |  -14.44893   3.971614    -3.64   0.000    -22.24845    -6.64941
     F7event_bX_n |          0  (omitted)
     F6event_aX_n |          0  (omitted)
     F6event_bX_n |          0  (omitted)
     F5event_aX_n |          0  (omitted)
     F5event_bX_n |          0  (omitted)
     F4event_aX_n |          0  (omitted)
     F4event_bX_n |          0  (omitted)
     F3event_aX_n |          0  (omitted)
     F3event_bX_n |          0  (omitted)
     F2event_aX_n |          0  (omitted)
     F2event_bX_n |          0  (omitted)
     L0event_aX_n |          0  (omitted)
     L0event_bX_n |          0  (omitted)
     L1event_aX_n |          0  (omitted)
     L1event_bX_n |          0  (omitted)
     L2event_aX_n |          0  (omitted)
     L2event_bX_n |          0  (omitted)
     L3event_aX_n |          0  (omitted)
     L3event_bX_n |          0  (omitted)
     L4event_aX_n |          0  (omitted)
     L4event_bX_n |          0  (omitted)
     L5event_aX_n |          0  (omitted)
     L5event_bX_n |          0  (omitted)
     L6event_aX_n |          0  (omitted)
     L6event_bX_n |          0  (omitted)
     L7event_aX_n |          0  (omitted)
     L7event_bX_n |   2.125042   1.560613     1.36   0.174    -.9397152    5.189799
        F1eventRS |          0  (omitted)
     F1event_aX_n |          0  (omitted)
     F1event_bX_n |          0  (omitted)
        ln_ew_ges |   2.183248    1.06528     2.05   0.041     .0912333    4.275263
         ew_biodt |   .3963148   .0268712    14.75   0.000     .3435447    .4490849
        ew_dtmihi |  -.1891711   .0544886    -3.47   0.001    -.2961767   -.0821655
         ew_ledig |   .2427535    .055446     4.38   0.000     .1338678    .3516391
       ew_married |   .2914349   .0575786     5.06   0.000     .1783611    .4045087
        wb_anteil |  -.2580572   .0215684   -11.96   0.000    -.3004136   -.2157009
          wb_ausl |  -.0778844   .0147103    -5.29   0.000    -.1067728   -.0489961
         wb_18t24 |  -.0157367    .026614    -0.59   0.555    -.0680017    .0365282
         wb_25t34 |   .0374252   .0169396     2.21   0.028      .004159    .0706914
         wb_35t44 |  -.0067004   .0218958    -0.31   0.760    -.0496997    .0362988
         wb_45t59 |  -.0403528   .0191952    -2.10   0.036    -.0780486    -.002657
          avg_dur |   .0370261   .0214849     1.72   0.085    -.0051664    .0792185
          hh_kids |  -.0915938   .0381516    -2.40   0.017    -.1665166   -.0166711
mpreis_flats_rent |  -.0196268   .0213954    -0.92   0.359    -.0616435      .02239
            _cons |  -13.05536   9.136955    -1.43   0.154    -30.99866    4.887944
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(MWFE estimator converged in 5 iterations)
note: F7event_bX_n omitted because of collinearity
note: F6event_aX_n omitted because of collinearity
note: F6event_bX_n omitted because of collinearity
note: F5event_aX_n omitted because of collinearity
note: F5event_bX_n omitted because of collinearity
note: F4event_aX_n omitted because of collinearity
note: F4event_bX_n omitted because of collinearity
note: F3event_aX_n omitted because of collinearity
note: F3event_bX_n omitted because of collinearity
note: F2event_aX_n omitted because of collinearity
note: F2event_bX_n omitted because of collinearity
note: L0event_aX_n omitted because of collinearity
note: L0event_bX_n omitted because of collinearity
note: L1event_aX_n omitted because of collinearity
note: L1event_bX_n omitted because of collinearity
note: L2event_aX_n omitted because of collinearity
note: L2event_bX_n omitted because of collinearity
note: L3event_aX_n omitted because of collinearity
note: L3event_bX_n omitted because of collinearity
note: L4event_aX_n omitted because of collinearity
note: L4event_bX_n omitted because of collinearity
note: L5event_aX_n omitted because of collinearity
note: L5event_bX_n omitted because of collinearity
note: L6event_aX_n omitted because of collinearity
note: L6event_bX_n omitted because of collinearity
note: L7event_aX_n omitted because of collinearity
note: F1eventRS omitted because of collinearity
note: F1event_aX_n omitted because of collinearity
note: F1event_bX_n omitted because of collinearity

HDFE Linear regression                            Number of obs   =      4,944
Absorbing 2 HDFE groups                           F(  56,    617) =      27.27
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9899
                                                  Adj R-squared   =     0.9877
                                                  Within R-sq.    =     0.4534
Number of clusters (sb_new)  =        618         Root MSE        =     1.6418

                                    (Std. err. adjusted for 618 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7eventRS |   .0516767   .4162139     0.12   0.901    -.7656909    .8690443
        F6eventRS |   .3533759   .3680222     0.96   0.337    -.3693521    1.076104
        F5eventRS |  -.2980049   .3620356    -0.82   0.411    -1.008976    .4129665
        F4eventRS |  -.1790267   .2248451    -0.80   0.426    -.6205811    .2625276
        F3eventRS |  -.0182847   .2055375    -0.09   0.929    -.4219227    .3853533
        F2eventRS |   .1117974    .173971     0.64   0.521    -.2298496    .4534444
        L0eventRS |  -.3903507   .2134921    -1.83   0.068      -.80961    .0289085
        L1eventRS |  -.2118042   .2749924    -0.77   0.441    -.7518387    .3282303
        L2eventRS |   .1769768   .2793623     0.63   0.527    -.3716394    .7255929
        L3eventRS |   .2453151   .3066473     0.80   0.424    -.3568839    .8475142
        L4eventRS |      .9042   .6199596     1.46   0.145    -.3132867    2.121687
        L5eventRS |   1.617059   .6352846     2.55   0.011     .3694763    2.864641
        L6eventRS |    2.33629   .8816311     2.65   0.008     .6049284    4.067651
        L7eventRS |   2.169962   .8070237     2.69   0.007     .5851157    3.754808
     F7event_aX_n |  -1.298875   1.195459    -1.09   0.278    -3.646536    1.048786
     F7event_bX_n |   .1661992   .8189705     0.20   0.839    -1.442108    1.774507
     F6event_aX_n |   -1.03376   1.174158    -0.88   0.379     -3.33959    1.272071
     F6event_bX_n |  -.3338752   .7236364    -0.46   0.645    -1.754964    1.087214
     F5event_aX_n |   .9698705   1.099128     0.88   0.378    -1.188616    3.128357
     F5event_bX_n |   .8564508   .7772181     1.10   0.271    -.6698628    2.382764
     F4event_aX_n |   .5142588   .8797146     0.58   0.559    -1.213339    2.241857
     F4event_bX_n |   .0844304    .519223     0.16   0.871    -.9352283    1.104089
     F3event_aX_n |   .5443376   .7385234     0.74   0.461    -.9059866    1.994662
     F3event_bX_n |  -.2931498   .5258894    -0.56   0.577      -1.3259    .7396003
     F2event_aX_n |  -.5153951   .6285974    -0.82   0.413    -1.749845    .7190548
     F2event_bX_n |  -.2282207   .4303657    -0.53   0.596     -1.07338    .6169385
     L0event_aX_n |     1.5536   .7292136     2.13   0.034      .121558    2.985641
     L0event_bX_n |  -.9249603    .593875    -1.56   0.120    -2.091222    .2413011
     L1event_aX_n |   1.285177   .8132647     1.58   0.115    -.3119254     2.88228
     L1event_bX_n |   .1006661   .6805101     0.15   0.882    -1.235731    1.437063
     L2event_aX_n |   1.183707   .7770511     1.52   0.128    -.3422787    2.709693
     L2event_bX_n |  -.2265635   .7464889    -0.30   0.762    -1.692531    1.239404
     L3event_aX_n |   .4196513   .8608948     0.49   0.626    -1.270988    2.110291
     L3event_bX_n |  -.3237576   .7389736    -0.44   0.661    -1.774966    1.127451
     L4event_aX_n |  -1.364268   2.845556    -0.48   0.632    -6.952418    4.223882
     L4event_bX_n |   .3567126   1.489077     0.24   0.811     -2.56756    3.280986
     L5event_aX_n |  -3.252924   1.761969    -1.85   0.065    -6.713107    .2072588
     L5event_bX_n |   .2140228   1.943504     0.11   0.912    -3.602663    4.030708
     L6event_aX_n |  -10.82279   5.131961    -2.11   0.035    -20.90101   -.7445579
     L6event_bX_n |  -2.470956   2.495034    -0.99   0.322    -7.370745    2.428833
     L7event_aX_n |  -8.449186   4.301756    -1.96   0.050    -16.89704   -.0013281
     F7event_bX_n |          0  (omitted)
     F6event_aX_n |          0  (omitted)
     F6event_bX_n |          0  (omitted)
     F5event_aX_n |          0  (omitted)
     F5event_bX_n |          0  (omitted)
     F4event_aX_n |          0  (omitted)
     F4event_bX_n |          0  (omitted)
     F3event_aX_n |          0  (omitted)
     F3event_bX_n |          0  (omitted)
     F2event_aX_n |          0  (omitted)
     F2event_bX_n |          0  (omitted)
     L0event_aX_n |          0  (omitted)
     L0event_bX_n |          0  (omitted)
     L1event_aX_n |          0  (omitted)
     L1event_bX_n |          0  (omitted)
     L2event_aX_n |          0  (omitted)
     L2event_bX_n |          0  (omitted)
     L3event_aX_n |          0  (omitted)
     L3event_bX_n |          0  (omitted)
     L4event_aX_n |          0  (omitted)
     L4event_bX_n |          0  (omitted)
     L5event_aX_n |          0  (omitted)
     L5event_bX_n |          0  (omitted)
     L6event_aX_n |          0  (omitted)
     L6event_bX_n |          0  (omitted)
     L7event_aX_n |          0  (omitted)
     L7event_bX_n |  -1.205318      3.028    -0.40   0.691    -7.151754    4.741119
        F1eventRS |          0  (omitted)
     F1event_aX_n |          0  (omitted)
     F1event_bX_n |          0  (omitted)
        ln_ew_ges |   1.440725   1.034466     1.39   0.164    -.5907756    3.472227
         ew_biodt |   .7716444   .0310183    24.88   0.000     .7107301    .8325587
        ew_dtmihi |  -.1579528   .0505245    -3.13   0.002    -.2571736    -.058732
         ew_ledig |   .4232651   .0639906     6.61   0.000     .2975993    .5489309
       ew_married |   .6576438   .0618726    10.63   0.000     .5361374    .7791502
        wb_anteil |  -.5429146   .0241024   -22.53   0.000    -.5902474   -.4955818
          wb_ausl |  -.0574526   .0176418    -3.26   0.001    -.0920978   -.0228074
         wb_18t24 |  -.0407193   .0255406    -1.59   0.111    -.0908764    .0094377
         wb_25t34 |  -.0126348   .0168353    -0.75   0.453    -.0456961    .0204266
         wb_35t44 |  -.0027769   .0204006    -0.14   0.892    -.0428398    .0372861
         wb_45t59 |   -.030138   .0193347    -1.56   0.120    -.0681079    .0078319
          avg_dur |   .0191262   .0212156     0.90   0.368    -.0225373    .0607897
          hh_kids |  -.1191193   .0344151    -3.46   0.001    -.1867042   -.0515345
mpreis_flats_rent |   .0196066   .0221458     0.89   0.376    -.0238835    .0630968
            _cons |   .8523406   9.340606     0.09   0.927    -17.49089    19.19557
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       618         618           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         
.         // PLOT:  Base levels
.         event_plot  turnout_urne turnout_pos_req turnout_tot_req , ///
>         stub_lag(L#eventRS ) stub_lead(F#eventRS ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.15(0.15)0.15) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4
> (1)2) xtitle("Election since reassignment",size(medsmall)) ///
>                 legend(pos(12)order(1 "Polling place turnout" 3 "Mail-in turnout" 5 "Total turno
> ut" ) row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel A.} Base Effects",nobox span bexpand justification(left) size(1
> 2pt)) ///
>                 subtitle("rescaled event-time dummies") ///
>                 name(DDD_base, replace)) ///
>         lag_opt1(msymbol(S) msize(3pt) color(navy))     lag_ci_opt1(color(navy)) ///
>         lag_opt2(msymbol(O) msize(3pt) color(cranberry))        lag_ci_opt2(color(cranberry)) //
> /  
>         lag_opt3(msymbol(Oh) msize(3pt) color(black))   lag_ci_opt3(color(black))               
>         

.         
.         // PLOT: Triple Diff: Increase
.         event_plot  turnout_urne turnout_pos_req turnout_tot_req , ///
>         stub_lag(L#event_bX_n ) stub_lead(F#event_bX_n ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.15(0.15)0.15) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4
> (1)2) xtitle("Election since reassignment",size(medsmall)) ///
>                 legend(pos(12)order(1 "Polling place turnout" 3 "Mail-in turnout" 5 "Total turno
> ut" ) row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel B.} Triple Difference Estimates",nobox span bexpand just(left) 
> size(12pt)) ///
>                 subtitle("{&Delta}distance {c 215} event-time dummies {c 215} {bf:1}[increase]")
>  ///
>                 name(DDD_up, replace)) ///
>         lag_opt1(msymbol(S) msize(3pt) color(navy))     lag_ci_opt1(color(navy)) ///
>         lag_opt2(msymbol(O) msize(3pt) color(cranberry))        lag_ci_opt2(color(cranberry)) //
> /  
>         lag_opt3(msymbol(Oh) msize(3pt) color(black))   lag_ci_opt3(color(black))

. 
.         
.         // PLOT: Triple Diff: Decrease
.         event_plot  turnout_urne turnout_pos_req turnout_tot_req , ///
>         stub_lag(L#event_aX_n ) stub_lead(F#event_aX_n ) plottype(scatter) ciplottype(rcap) ///
>         together perturb(-0.15(0.15)0.15) trimlead(4) trimlag(2) noautolegend ///
>         graph_opt(xtitle("") ytitle("Voter turnout in %""(estimates)", size(medsmall)) xlabel(-4
> (1)2) xtitle("Election since reassignment",size(medsmall)) ///
>                 legend(pos(12)order(1 "Polling place turnout" 3 "Mail-in turnout" 5 "Total turno
> ut" ) row(1) region(style(none))) ///
>                 xline(-0.5, lcolor(black) lpattern(solid)) yline(0, lcolor(gray) lpat(solid)) yl
> abel(, angle(horizontal)) ///
>                 title("{bf:Panel C.} Triple Difference Estimates",nobox span bexpand just(left) 
> size(12pt)) ///
>                 subtitle(" {&Delta}distance {c 215} event-time dummies {c 215} {bf:1}[decrease]"
> ) ///
>                 name(DDD_dwn, replace)) ///
>         lag_opt1(msymbol(S) msize(3pt) color(navy))     lag_ci_opt1(color(navy)) ///
>         lag_opt2(msymbol(O) msize(3pt) color(cranberry))        lag_ci_opt2(color(cranberry)) //
> /  
>         lag_opt3(msymbol(Oh) msize(3pt) color(black))   lag_ci_opt3(color(black))

.         
.         
.         * PLOT: FIGURE C10. Triple Difference Estimates by Increase and Decrease in Distance
.         grc1leg2  DDD_base DDD_up DDD_dwn  , xcommon  col(2) iscale(.7) ///
>         pos(4) ring(0) lcol(1) ltitle("Outcome:")  ///
>                                 lxoffset(-20) lyoffset(20) legscale(*.9) ltsize(*.8)
-grc1leg2- working...

Note: To preserve in a saved graphics file the legend's row and column rearrangements
made with the options -lrows()- and/or -lcols()-, specify the suboption -asis-
for the saving() option or for the graph save command.


.         gr_edit .legend.title.DragBy 0 -8

.         graph export "$figures/Figure_C10_ES_DDD_RSevent_delta_dist_2slopes.pdf", replace       
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_C10_ES
    > _DDD_RSevent_delta_dist_2slopes.pdf saved as PDF format

.         
.         
.  *** stats for manuscript
.         
.         * Effect of dist INCREASE in t=0
.         estimates restore turnout_urne 
(results turnout_urne are active now)

.         di      as text "A 1-km distance increase changes {bf: in-person turnout} in t=0 by:" //
> /
>                 as res %12.3f _b[L0event_bX_n] as text " precentage points"     
A 1-km distance increase changes  in-person turnout in t=0 by:      -3.841 precentage points

.                 
.         estimates restore turnout_tot_req
(results turnout_tot_req are active now)

.         di      as text "A 1-km distance increase changes {bf: total turnout} in t=0 by:" ///
>                 as res %12.3f _b[L0event_bX_n] as text " precentage points"                     
A 1-km distance increase changes  total turnout in t=0 by:      -0.925 precentage points

.         
.         * How much closer must the PP move to compensate turnout drop due to reassignment disuti
> lity?
.         estimates restore turnout_tot_req 
(results turnout_tot_req are active now)

.         di      as text "To offset reassignm. disutil in {bf: total turnout} in t=0, PP must mov
> e closer by:" ///
>                 as res %12.3f _b[ L0eventRS]/_b[L0event_aX_n] *(-1) as text " km"
To offset reassignm. disutil in  total turnout in t=0, PP must move closer by:       0.251 km

.         
.         estimates restore turnout_urne  
(results turnout_urne are active now)

.         di      as text "To offset reassignm. disutil in {bf:in-person turnout} in t=0, PP must 
> move closer by:" ///
>                 as res %12.3f _b[ L0eventRS]/_b[L0event_aX_n] *(-1) as text " km"       
To offset reassignm. disutil in in-person turnout in t=0, PP must move closer by:       0.141 km

.                 
.  ** Are the distance slopes statistically different for increases and decreases?
.         * PP turnout
.         estimates restore turnout_urne  
(results turnout_urne are active now)

.         // all post periods
.         lincom  (1/3)*(L0event_aX_n + L1event_aX_n + L2event_aX_n) - (1/3)*(L0event_bX_n + L1eve
> nt_bX_n + L2event_bX_n)*(-1)

 ( 1)  .3333333*L0event_aX_n + .3333333*L0event_bX_n + .3333333*L1event_aX_n +
       .3333333*L1event_bX_n + .3333333*L2event_aX_n + .3333333*L2event_bX_n = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.992552   1.279601     1.56   0.120    -.5203485    4.505453
------------------------------------------------------------------------------

.         // single post periods
.         lincom L0event_aX_n - L0event_bX_n *(-1)

 ( 1)  L0event_aX_n + L0event_bX_n = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    1.67069   1.485423     1.12   0.261    -1.246408    4.587789
------------------------------------------------------------------------------

.         lincom L1event_aX_n - L1event_bX_n *(-1)

 ( 1)  L1event_aX_n + L1event_bX_n = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   2.525658   1.424897     1.77   0.077     -.272578    5.323894
------------------------------------------------------------------------------

.         lincom L2event_aX_n - L2event_bX_n *(-1)

 ( 1)  L2event_aX_n + L2event_bX_n = 0

------------------------------------------------------------------------------
turnout_urne | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.781308   1.502159     1.19   0.236    -1.168656    4.731272
------------------------------------------------------------------------------

.         
.         * Mail-in turnout
.         estimates restore turnout_pos_req
(results turnout_pos_req are active now)

.         // all post periods
.         lincom  (1/3)*(L0event_aX_n + L1event_aX_n + L2event_aX_n) - (1/3)*(L0event_bX_n + L1eve
> nt_bX_n + L2event_bX_n)*(-1)

 ( 1)  .3333333*L0event_aX_n + .3333333*L0event_bX_n + .3333333*L1event_aX_n +
       .3333333*L1event_bX_n + .3333333*L2event_aX_n + .3333333*L2event_bX_n = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -1.002009   1.218249    -0.82   0.411    -3.394426    1.390409
------------------------------------------------------------------------------

.         // single post periods
.         lincom L0event_aX_n - L0event_bX_n *(-1)

 ( 1)  L0event_aX_n + L0event_bX_n = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -1.042047   1.274779    -0.82   0.414     -3.54548    1.461385
------------------------------------------------------------------------------

.         lincom L1event_aX_n - L1event_bX_n *(-1)

 ( 1)  L1event_aX_n + L1event_bX_n = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -1.139813   1.368133    -0.83   0.405    -3.826574    1.546948
------------------------------------------------------------------------------

.         lincom L2event_aX_n - L2event_bX_n *(-1)        

 ( 1)  L2event_aX_n + L2event_bX_n = 0

------------------------------------------------------------------------------
turnout_po~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.8241661    1.57166    -0.52   0.600    -3.910617    2.262285
------------------------------------------------------------------------------

.         
.         * total turnout
.         estimates restore turnout_tot_req       
(results turnout_tot_req are active now)

.         // all post periods
.         lincom  (1/3)*(L0event_aX_n + L1event_aX_n + L2event_aX_n) - (1/3)*(L0event_bX_n + L1eve
> nt_bX_n + L2event_bX_n)*(-1)

 ( 1)  .3333333*L0event_aX_n + .3333333*L0event_bX_n + .3333333*L1event_aX_n +
       .3333333*L1event_bX_n + .3333333*L2event_aX_n + .3333333*L2event_bX_n = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9905419   1.052457     0.94   0.347     -1.07629    3.057374
------------------------------------------------------------------------------

.         // single post periods
.         lincom L0event_aX_n - L0event_bX_n *(-1)

 ( 1)  L0event_aX_n + L0event_bX_n = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .6286392   1.111444     0.57   0.572    -1.554033    2.811312
------------------------------------------------------------------------------

.         lincom L1event_aX_n - L1event_bX_n *(-1)

 ( 1)  L1event_aX_n + L1event_bX_n = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   1.385843   1.269981     1.09   0.276    -1.108165    3.879852
------------------------------------------------------------------------------

.         lincom L2event_aX_n - L2event_bX_n *(-1)        

 ( 1)  L2event_aX_n + L2event_bX_n = 0

------------------------------------------------------------------------------
turnout_to~q | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .9571434   1.283456     0.75   0.456    -1.563328    3.477615
------------------------------------------------------------------------------

.         
. 
end of do-file
Running: 04j_rob_balanced_smpl_tables_e8_e9.do

. /*
> 
> Input: newdata/estimation_prep_ltw18 [prepared precinct-level panel]
> 
> Output: Table E.8, E.9 
> 
> Tasks: Robustness to balanced ES on different periods
>         
> */      
.         
. * PULL: Precinct-level data
.         use "$newdata/estimation_prep_ltw18.dta", clear

. 
.         
. ********************************************************************************
.         // Prep Estimation //
. ********************************************************************************
.         
.         // Relabel outcomes for tables 
.         lab var turnout_urne    "\multicolumn{6}{c}{Polling Place Turnout}"

.         lab var turnout_pos_req "\multicolumn{6}{c}{Mail-in turnout}"

.         lab var turnout_tot_req "\multicolumn{6}{c}{Total turnout}"             

.         
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen     F`l'event = K==-`l'
  3.                 lab var F`l'event "Reassignment (#t-`l'#)"
  4.         }       

.         forvalues l = 0/7 {
  2.                 gen     L`l'event = K==`l'
  3.                 lab var L`l'event "Reassignment (#t+`l'#)"
  4.         }       

.         order F1event, last     

.         
.         // compute group ids for DISTANCE increase/decrease, 0 else
.         cap drop tmp*

.         gen     tmp = (del_street_dist>0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_up = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_up = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_up "=1 if dist increase in event, 0 else"

.         
.         cap drop tmp*

.         gen     tmp = (del_street_dist<0)                       if K==0
(4,664 missing values generated)

.         bys sb_new (tmp): gen ind_dist_dn = tmp[1]
(2,704 missing values generated)

.         replace ind_dist_dn = 0                                         if missing(Ei)
(2,704 real changes made)

.         lab var ind_dist_dn "=1 if dist decrease in event, 0 else"

. 
.         
. ** Sample composition
.         // Bsl sample 
.         fre Ei if K==0 & fulltottreat100<=1 

Ei -- date of treatment,treat=100% reassigned, NT=.
-------------------------------------------------------------
                |      Freq.    Percent      Valid       Cum.
----------------+--------------------------------------------
Valid   1 LTW13 |          4       2.67       2.67       2.67
        3 KOW14 |          6       4.00       4.00       6.67
        5 BTW17 |         81      54.00      54.00      60.67
        6 LTW18 |         13       8.67       8.67      69.33
        7 EUW19 |          6       4.00       4.00      73.33
        8 KOW20 |         40      26.67      26.67     100.00
        Total   |        150     100.00     100.00           
-------------------------------------------------------------

.                 /*
>                                                 |      Freq.    Percent 
>                                 ---------+-----------------------
>                                 1 LTW13 |          4       2.67 
>                                 3 KOW14 |          6       4.00 
>                                 5 BTW17 |         81      54.00 
>                                 6 LTW18 |         13       8.67 
>                                 7 EUW19 |          6       4.00 
>                                 8 KOW20 |         40      26.67 
>                                 Total   |                150     100.00
>                                 -------------------------------
>                 */
.         // Bal around t in [-4,0]
.         fre Ei if K==0 & smpl_bal_tp0 & fulltottreat100<=1 

Ei -- date of treatment,treat=100% reassigned, NT=.
-------------------------------------------------------------
                |      Freq.    Percent      Valid       Cum.
----------------+--------------------------------------------
Valid   3 KOW14 |          6       4.11       4.11       4.11
        5 BTW17 |         81      55.48      55.48      59.59
        6 LTW18 |         13       8.90       8.90      68.49
        7 EUW19 |          6       4.11       4.11      72.60
        8 KOW20 |         40      27.40      27.40     100.00
        Total   |        146     100.00     100.00           
-------------------------------------------------------------

.         /*
>                                 -------------------------------
>                                                 |      Freq.    Percent
>                                 ---------+---------------------
>                                 3 KOW14 |          6       4.11
>                                 5 BTW17 |         81      55.48
>                                 6 LTW18 |         13       8.90
>                                 7 EUW19 |          6       4.11
>                                 8 KOW20 |         40      27.40
>                                 Total   |        146     100.00
>                                 -------------------------------
>         */      
.         
.         // Bal around t in [-4,+1]
.         fre Ei if K==0 & smpl_bal_tp1==1 & fulltottreat100<=1 

Ei -- date of treatment,treat=100% reassigned, NT=.
-------------------------------------------------------------
                |      Freq.    Percent      Valid       Cum.
----------------+--------------------------------------------
Valid   5 BTW17 |         81      81.00      81.00      81.00
        6 LTW18 |         13      13.00      13.00      94.00
        7 EUW19 |          6       6.00       6.00     100.00
        Total   |        100     100.00     100.00           
-------------------------------------------------------------

.         /*
>                                                 |      Freq.    Percent   
>                                 ---------+------------------------
>                                 5 BTW17 |         81      81.00   
>                                 6 LTW18 |         13      13.00   
>                                 7 EUW19 |          6       6.00   
>                                 Total   |        100     100.00   
>                                 ----------------------------------
>         */
. 
.         
.         //Bal around t in [-2,+2]
.         fre Ei if K==0 & smpl_bal_tm2==1 & fulltottreat100<=1

Ei -- date of treatment,treat=100% reassigned, NT=.
-------------------------------------------------------------
                |      Freq.    Percent      Valid       Cum.
----------------+--------------------------------------------
Valid   3 KOW14 |          6       6.00       6.00       6.00
        5 BTW17 |         81      81.00      81.00      87.00
        6 LTW18 |         13      13.00      13.00     100.00
        Total   |        100     100.00     100.00           
-------------------------------------------------------------

.         /*
>                                                 |      Freq.    Percent  
>                                 ----------+----------------------
>                                 3 KOW14 |          6       6.00  
>                                 5 BTW17 |         81      81.00  
>                                 6 LTW18 |         13      13.00  
>                                 Total   |        100     100.00  
>                                 ---------------------------------
>         */
.         
.         // Bal around t in [-2,+1] 
.         fre Ei if K==0 & smpl_bal_tpm==1 & fulltottreat100<=1

Ei -- date of treatment,treat=100% reassigned, NT=.
-------------------------------------------------------------
                |      Freq.    Percent      Valid       Cum.
----------------+--------------------------------------------
Valid   3 KOW14 |          6       5.66       5.66       5.66
        5 BTW17 |         81      76.42      76.42      82.08
        6 LTW18 |         13      12.26      12.26      94.34
        7 EUW19 |          6       5.66       5.66     100.00
        Total   |        106     100.00     100.00           
-------------------------------------------------------------

.         /*
>                         ---------------------------------
>                                         |      Freq.    Percent  
>                         ---------+-----------------------
>                         3 KOW14 |          6       5.66  
>                         5 BTW17 |         81      76.42  
>                         6 LTW18 |         13      12.26  
>                         7 EUW19 |          6       5.66  
>                         Total   |        106     100.00  
>                         ---------------------------------
>         */
.         
.         // Bal around t in [-4,+2]
.         fre Ei if K==0 & smpl_bal==1 & fulltottreat100<=1 

Ei -- date of treatment,treat=100% reassigned, NT=.
-------------------------------------------------------------
                |      Freq.    Percent      Valid       Cum.
----------------+--------------------------------------------
Valid   5 BTW17 |         81      86.17      86.17      86.17
        6 LTW18 |         13      13.83      13.83     100.00
        Total   |         94     100.00     100.00           
-------------------------------------------------------------

.         /*
>                                         |      Freq.    Percent  
>                         --------+------------------------
>                         5 BTW17 |         81      86.17  
>                         6 LTW18 |         13      13.83  
>                         Total   |                 94     100.00
>                         ---------------------------------
>         */      
. 
. 
. 
. ********************************************************************************
. //       Balanced estimates for baseline specification //
. ********************************************************************************        
.         
.         local smplif "& fulltottreat100<=1"

.         
.         estimates clear 

.         outreg, clear

.         
.         local j=0

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                 
.         // Basaeline (unbalanced)
.         local `++j'
  3.                 reghdfe `v' F7event-L7event F1event  $ctr $wgt ///
>                                 if fulltottreat100<=1 ,         absorb(i.wahl_id#i.stadtbez i.sb
> _new) cluster(sb_new)
  4.                 
.                 estimates store `v'_bsl 
  5.                 extract_N 100
  6.                 qui outreg,  $opt  keep(F4event-L2event)  ///
>                         addrow("Unbalanced sample", X \ #treated precincts, `r(N_T)' \ #control 
> precincts, `r(N_C)') ctitle("", "(`j')")
  7.                 
.         // Balanced around -4/0 
.         local `++j'
  8.                 reghdfe `v' F7event-L7event F1event  $ctr $wgt ///
>                                 if smpl_bal_tp0==1 `smplif',    absorb(i.wahl_id#i.stadtbez i.sb
> _new) cluster(sb_new)
  9. 
.                 estimates store `v'_tp0 
 10.                 extract_N 100
 11.                 qui outreg,  $opt  keep(F4event-L2event)  ///
>                         addrow("Balanced sample, $ \tau \in$", "$ [-4,0] $" \ #treated precincts
> , `r(N_T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')")
 12.                 
.         // Balanced around -2/+1
.         local `++j'
 13.                 reghdfe `v' F7event-L7event F1event $ctr $wgt ///
>                                         if  smpl_bal_tpm==1 `smplif', absorb(i.wahl_id#i.stadtbe
> z i.sb_new) cluster(sb_new)
 14. 
.                 estimates store `v'_tpm 
 15.                 extract_N 100
 16.                 qui outreg,  $opt  keep(F4event-L2event)  ///
>                         addrow("Balanced sample, $ \tau \in$", "$ [-2,1] $" \ #treated precincts
> , `r(N_T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')")
 17.                         
.         // Balanced around -4/+1
.         local `++j'
 18.                 reghdfe `v' F7event-L7event F1event $ctr $wgt ///
>                                         if  smpl_bal_tp1==1 `smplif' , absorb(i.wahl_id#i.stadtb
> ez i.sb_new) cluster(sb_new)
 19. 
.                 estimates store `v'_tp1
 20.                 extract_N 100
 21.                 qui outreg,  $opt  keep(F4event-L2event)  ///
>                         addrow("Balanced sample, $ \tau \in$", "$ [-4,1] $" \ #treated precincts
> , `r(N_T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')" \"", "`:var lab `v''")
 22.         
.         // Balanced around -2/+2
.         local `++j'
 23.                 reghdfe `v' F7event-L7event F1event $ctr $wgt ///
>                                         if  smpl_bal_tm2==1 `smplif', absorb(i.wahl_id#i.stadtbe
> z i.sb_new) cluster(sb_new)
 24. 
.                 estimates store `v'_tm2
 25.                 extract_N 100
 26.                 qui outreg,  $opt  keep(F4event-L2event)  ///
>                         addrow("Balanced sample, $ \tau \in$", "$ [-2,2] $" \ #treated precincts
> , `r(N_T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')")
 27.                 
.         // Balanced around -4/+2
.         local `++j'
 28.                 reghdfe `v' F7event-L7event F1event  $ctr $wgt ///
>                                 if smpl_bal==1 `smplif' , absorb(i.wahl_id#i.stadtbez i.sb_new) 
> cluster(sb_new)
 29. 
.                 estimates store `v'_bal
 30.                 extract_N 100
 31.                 qui outreg,  $opt  keep(F4event-L2event)  ///
>                         addrow("Balanced sample, $ \tau \in$", "$ [-4,2] $" \#treated precincts,
>  `r(N_T)' \ #control precincts, `r(N_C)')       ctitle("", "(`j')")
 32.         }       
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  28,    487) =      15.19
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9741
                                                  Adj R-squared   =     0.9683
                                                  Within R-sq.    =     0.1681
Number of clusters (sb_new)  =        488         Root MSE        =     1.6505

                                    (Std. err. adjusted for 488 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.2166858   .3689328    -0.59   0.557    -.9415822    .5082107
          F6event |  -.0145619   .3426901    -0.04   0.966    -.6878956    .6587717
          F5event |   .2164362   .2866539     0.76   0.451    -.3467949    .7796673
          F4event |   .0292416   .2059159     0.14   0.887    -.3753517    .4338349
          F3event |  -.1784292   .2097541    -0.85   0.395     -.590564    .2337056
          F2event |  -.0396256   .1659148    -0.24   0.811    -.3656227    .2863716
          L0event |   -1.14249   .2822242    -4.05   0.000    -1.697017   -.5879621
          L1event |  -1.322708    .278782    -4.74   0.000    -1.870472   -.7749442
          L2event |  -.8352131   .2931427    -2.85   0.005    -1.411194   -.2592325
          L3event |  -.4402946   .2903295    -1.52   0.130    -1.010748    .1301585
          L4event |  -1.208415   .6092294    -1.98   0.048    -2.405458   -.0113725
          L5event |  -.8684569   .6501734    -1.34   0.182    -2.145948    .4090343
          L6event |   .8034572    .784604     1.02   0.306    -.7381697    2.345084
          L7event |   .6006363   1.231056     0.49   0.626      -1.8182    3.019472
          F1event |          0  (omitted)
        ln_ew_ges |  -1.637554   1.011954    -1.62   0.106    -3.625888    .3507809
         ew_biodt |   .3633102   .0298592    12.17   0.000     .3046416    .4219789
        ew_dtmihi |   .0344989   .0540724     0.64   0.524    -.0717451    .1407428
         ew_ledig |   .1833252   .0660072     2.78   0.006     .0536312    .3130193
       ew_married |   .3642623   .0657864     5.54   0.000      .235002    .4935225
        wb_anteil |  -.2809773   .0224158   -12.53   0.000     -.325021   -.2369337
          wb_ausl |   .0307206   .0157118     1.96   0.051    -.0001508     .061592
         wb_18t24 |  -.0120712   .0318266    -0.38   0.705    -.0746056    .0504633
         wb_25t34 |  -.0604665   .0202939    -2.98   0.003     -.100341    -.020592
         wb_35t44 |  -.0012956    .023986    -0.05   0.957    -.0484245    .0458332
         wb_45t59 |   .0239382   .0226622     1.06   0.291    -.0205897     .068466
          avg_dur |  -.0365692   .0216322    -1.69   0.092    -.0790731    .0059347
          hh_kids |    .005937   .0414415     0.14   0.886    -.0754893    .0873633
mpreis_flats_rent |   .0333113   .0277469     1.20   0.231    -.0212071    .0878297
            _cons |   20.86432   9.389682     2.22   0.027     2.415033    39.31361
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       488         488           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,872
Absorbing 2 HDFE groups                           F(  26,    483) =      15.49
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9741
                                                  Adj R-squared   =     0.9683
                                                  Within R-sq.    =     0.1657
Number of clusters (sb_new)  =        484         Root MSE        =     1.6521

                                    (Std. err. adjusted for 484 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.2317213   .3693151    -0.63   0.531    -.9573839    .4939414
          F6event |  -.0284753   .3429822    -0.08   0.934    -.7023968    .6454463
          F5event |    .212609    .286969     0.74   0.459    -.3512528    .7764709
          F4event |   .0158759   .2058812     0.08   0.939    -.3886576    .4204094
          F3event |  -.1943075   .2098803    -0.93   0.355    -.6066988    .2180837
          F2event |  -.0340245   .1665947    -0.20   0.838    -.3613644    .2933153
          L0event |  -1.145995   .2840318    -4.03   0.000    -1.704086   -.5879046
          L1event |  -1.309845    .282426    -4.64   0.000    -1.864781   -.7549098
          L2event |  -.8597398   .2957704    -2.91   0.004    -1.440895   -.2785843
          L3event |  -.4322993   .2943182    -1.47   0.143    -1.010602    .1460029
          L4event |   -1.52173   .7963099    -1.91   0.057    -3.086389      .04293
          L5event |  -.9694559   .9530509    -1.02   0.310    -2.842094     .903182
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |  -1.613425   1.009843    -1.60   0.111    -3.597652     .370803
         ew_biodt |   .3618223   .0300042    12.06   0.000     .3028674    .4207772
        ew_dtmihi |   .0321307   .0542617     0.59   0.554    -.0744875    .1387489
         ew_ledig |   .1800203   .0664758     2.71   0.007     .0494028    .3106378
       ew_married |   .3575177   .0662346     5.40   0.000     .2273742    .4876612
        wb_anteil |  -.2772199   .0224644   -12.34   0.000    -.3213598   -.2330799
          wb_ausl |   .0296531   .0158558     1.87   0.062    -.0015017    .0608079
         wb_18t24 |  -.0115499   .0319115    -0.36   0.718    -.0742523    .0511525
         wb_25t34 |  -.0608653   .0204819    -2.97   0.003    -.1011098   -.0206207
         wb_35t44 |  -.0005031   .0240298    -0.02   0.983     -.047719    .0467129
         wb_45t59 |   .0239005   .0227436     1.05   0.294    -.0207882    .0685892
          avg_dur |  -.0370572     .02165    -1.71   0.088    -.0795971    .0054826
          hh_kids |   .0068345   .0415649     0.16   0.869    -.0748359    .0885048
mpreis_flats_rent |   .0310478   .0278373     1.12   0.265    -.0236493    .0857449
            _cons |   21.00786   9.393692     2.24   0.026     2.550315    39.46541
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       484         484           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        146        146

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,552
Absorbing 2 HDFE groups                           F(  25,    443) =      17.15
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9746
                                                  Adj R-squared   =     0.9688
                                                  Within R-sq.    =     0.1743
Number of clusters (sb_new)  =        444         Root MSE        =     1.6402

                                    (Std. err. adjusted for 444 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |   .7323999   .4127533     1.77   0.077     -.078798    1.543598
          F5event |   .7703015   .4071843     1.89   0.059    -.0299514    1.570554
          F4event |   .0020793   .2269421     0.01   0.993    -.4439376    .4480961
          F3event |  -.0935203   .2445346    -0.38   0.702    -.5741123    .3870718
          F2event |   .1310838   .1960483     0.67   0.504    -.2542165     .516384
          L0event |   -1.52975   .3306686    -4.63   0.000    -2.179624   -.8798756
          L1event |  -1.345027   .3046752    -4.41   0.000    -1.943815   -.7462386
          L2event |  -.8700743   .3160167    -2.75   0.006    -1.491152   -.2489961
          L3event |  -.3997823   .2992512    -1.34   0.182    -.9879106    .1883461
          L4event |  -1.534256   .8155577    -1.88   0.061    -3.137099    .0685871
          L5event |  -.9733676   .9663369    -1.01   0.314    -2.872542    .9258066
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |  -1.064082   1.003214    -1.06   0.289    -3.035732     .907569
         ew_biodt |   .3677476   .0297029    12.38   0.000     .3093716    .4261237
        ew_dtmihi |   .0191272   .0564532     0.34   0.735    -.0918221    .1300765
         ew_ledig |   .1867109   .0683758     2.73   0.007     .0523297     .321092
       ew_married |   .3572303   .0685394     5.21   0.000     .2225276    .4919331
        wb_anteil |  -.2787131   .0215308   -12.94   0.000    -.3210283   -.2363979
          wb_ausl |   .0313472   .0167919     1.87   0.063    -.0016545     .064349
         wb_18t24 |  -.0116805   .0358247    -0.33   0.745    -.0820881     .058727
         wb_25t34 |  -.0641649   .0218021    -2.94   0.003    -.1070132   -.0213166
         wb_35t44 |   .0007992   .0259318     0.03   0.975    -.0501655    .0517639
         wb_45t59 |   .0307954   .0227784     1.35   0.177    -.0139718    .0755627
          avg_dur |  -.0316172   .0215971    -1.46   0.144    -.0740628    .0108284
          hh_kids |  -.0085606   .0441929    -0.19   0.846    -.0954143    .0782932
mpreis_flats_rent |   .0413476   .0284651     1.45   0.147    -.0145959    .0972911
            _cons |   16.24181   9.633334     1.69   0.092    -2.690904    35.17452
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       444         444           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        106        106

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  23,    437) =      18.79
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9747
                                                  Adj R-squared   =     0.9688
                                                  Within R-sq.    =     0.1777
Number of clusters (sb_new)  =        438         Root MSE        =     1.6429

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |   .6753922   .4160967     1.62   0.105    -.1424073    1.493192
          F5event |   .7250541   .4111398     1.76   0.079    -.0830032    1.533111
          F4event |  -.0354408   .2341113    -0.15   0.880    -.4955648    .4246832
          F3event |   -.128085   .2560918    -0.50   0.617    -.6314097    .3752398
          F2event |   .1445969   .2055615     0.70   0.482    -.2594152    .5486091
          L0event |  -1.668988   .3440706    -4.85   0.000    -2.345227   -.9927495
          L1event |  -1.454671   .3213015    -4.53   0.000    -2.086159   -.8231827
          L2event |  -.8614285   .3336817    -2.58   0.010    -1.517249   -.2056081
          L3event |   -.362295   .3117356    -1.16   0.246    -.9749824    .2503925
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |  -1.052216   1.000583    -1.05   0.294     -3.01877    .9143372
         ew_biodt |   .3697458   .0298627    12.38   0.000     .3110535    .4284381
        ew_dtmihi |   .0170655   .0565807     0.30   0.763    -.0941386    .1282696
         ew_ledig |   .1750557   .0689247     2.54   0.011     .0395905    .3105209
       ew_married |   .3584576   .0688425     5.21   0.000      .223154    .4937613
        wb_anteil |  -.2798351   .0216016   -12.95   0.000    -.3222911   -.2373791
          wb_ausl |   .0323543   .0168221     1.92   0.055     -.000708    .0654165
         wb_18t24 |  -.0070171    .036245    -0.19   0.847    -.0782534    .0642191
         wb_25t34 |  -.0623126   .0219455    -2.84   0.005    -.1054443   -.0191808
         wb_35t44 |   .0029442    .025983     0.11   0.910     -.048123    .0540114
         wb_45t59 |   .0321579   .0228602     1.41   0.160    -.0127717    .0770875
          avg_dur |  -.0317496   .0216356    -1.47   0.143    -.0742723    .0107731
          hh_kids |  -.0066934   .0443122    -0.15   0.880    -.0937849    .0803981
mpreis_flats_rent |   .0416548   .0284313     1.47   0.144    -.0142242    .0975338
            _cons |   16.44893   9.674169     1.70   0.090    -2.564752    35.46261
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  24,    437) =      16.54
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9746
                                                  Adj R-squared   =     0.9687
                                                  Within R-sq.    =     0.1700
Number of clusters (sb_new)  =        438         Root MSE        =     1.6420

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |   .7410362   .4951408     1.50   0.135    -.2321171     1.71419
          F4event |   .0727675   .2382309     0.31   0.760    -.3954532    .5409883
          F3event |   .0488591   .2457711     0.20   0.843    -.4341812    .5318994
          F2event |   .1727248   .2024129     0.85   0.394    -.2250989    .5705485
          L0event |   -1.42382   .3484716    -4.09   0.000    -2.108709   -.7389319
          L1event |  -1.289701   .3234635    -3.99   0.000    -1.925439   -.6539638
          L2event |   -.814862   .3191617    -2.55   0.011    -1.442145   -.1875793
          L3event |  -.3207925   .2999066    -1.07   0.285    -.9102311     .268646
          L4event |  -1.490574   .8181176    -1.82   0.069    -3.098508    .1173607
          L5event |  -.9174354   .9674352    -0.95   0.343     -2.81884    .9839688
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   -1.05691   1.012438    -1.04   0.297    -3.046763     .932943
         ew_biodt |   .3670438   .0299811    12.24   0.000     .3081188    .4259687
        ew_dtmihi |   .0183868   .0575143     0.32   0.749    -.0946523    .1314258
         ew_ledig |   .1880918   .0686248     2.74   0.006     .0532161    .3229675
       ew_married |   .3549234   .0691298     5.13   0.000     .2190552    .4907915
        wb_anteil |  -.2772558   .0217957   -12.72   0.000    -.3200932   -.2344183
          wb_ausl |   .0294965   .0168298     1.75   0.080    -.0035809    .0625738
         wb_18t24 |  -.0128532   .0363865    -0.35   0.724    -.0843676    .0586612
         wb_25t34 |  -.0654539   .0218786    -2.99   0.003    -.1084543   -.0224536
         wb_35t44 |   .0031603   .0260706     0.12   0.904    -.0480791    .0543997
         wb_45t59 |   .0262003   .0229646     1.14   0.255    -.0189344     .071335
          avg_dur |  -.0305096   .0219009    -1.39   0.164    -.0735537    .0125346
          hh_kids |    -.00761   .0464066    -0.16   0.870    -.0988179     .083598
mpreis_flats_rent |    .040295   .0286812     1.40   0.161    -.0160753    .0966653
            _cons |   16.22171   9.798098     1.66   0.099    -3.035541    35.47897
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  22,    431) =      18.13
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9746
                                                  Adj R-squared   =     0.9687
                                                  Within R-sq.    =     0.1733
Number of clusters (sb_new)  =        432         Root MSE        =     1.6448

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |   .7022077   .4992803     1.41   0.160    -.2791194    1.683535
          F4event |   .0368231   .2462464     0.15   0.881      -.44717    .5208163
          F3event |   .0171021   .2589473     0.07   0.947    -.4918544    .5260587
          F2event |   .1914174   .2129712     0.90   0.369    -.2271741    .6100088
          L0event |  -1.568019   .3651881    -4.29   0.000    -2.285791   -.8502483
          L1event |  -1.407149   .3441269    -4.09   0.000    -2.083525   -.7307732
          L2event |  -.8044975   .3382545    -2.38   0.018    -1.469331   -.1396639
          L3event |  -.2790379    .313255    -0.89   0.374    -.8947355    .3366596
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |  -1.046892   1.010044    -1.04   0.301    -3.032116     .938332
         ew_biodt |   .3692613   .0301365    12.25   0.000     .3100285    .4284942
        ew_dtmihi |   .0164704   .0576431     0.29   0.775    -.0968261    .1297669
         ew_ledig |   .1765236    .069155     2.55   0.011     .0406005    .3124467
       ew_married |   .3561866   .0694503     5.13   0.000     .2196832      .49269
        wb_anteil |  -.2784894   .0218632   -12.74   0.000    -.3214612   -.2355176
          wb_ausl |   .0305717   .0168571     1.81   0.070    -.0025606     .063704
         wb_18t24 |  -.0083516    .036834    -0.23   0.821    -.0807482     .064045
         wb_25t34 |  -.0634937   .0220284    -2.88   0.004    -.1067902   -.0201971
         wb_35t44 |   .0053987   .0261243     0.21   0.836    -.0459481    .0567455
         wb_45t59 |   .0277837   .0230605     1.20   0.229    -.0175413    .0731087
          avg_dur |  -.0305989   .0219447    -1.39   0.164    -.0737307     .012533
          hh_kids |  -.0057824   .0465268    -0.12   0.901      -.09723    .0856652
mpreis_flats_rent |   .0407582   .0286592     1.42   0.156     -.015571    .0970875
            _cons |   16.41806   9.839683     1.67   0.096     -2.92167    35.75779
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  28,    487) =      16.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9627
                                                  Adj R-squared   =     0.9544
                                                  Within R-sq.    =     0.2299
Number of clusters (sb_new)  =        488         Root MSE        =     1.6447

                                    (Std. err. adjusted for 488 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0656777   .3421111     0.19   0.848    -.6065183    .7378738
          F6event |   .3294761   .2721114     1.21   0.227    -.2051813    .8641334
          F5event |  -.6535561    .319596    -2.04   0.041    -1.281513   -.0255988
          F4event |   -.212002   .2041237    -1.04   0.300    -.6130739    .1890699
          F3event |   .2247147   .1769645     1.27   0.205    -.1229935     .572423
          F2event |  -.1119185   .1474277    -0.76   0.448    -.4015913    .1777543
          L0event |   .6149413   .2614541     2.35   0.019      .101224    1.128659
          L1event |   1.141033   .2658581     4.29   0.000     .6186625    1.663403
          L2event |    1.18335   .3041661     3.89   0.000     .5857102     1.78099
          L3event |   .4937275   .2875306     1.72   0.087    -.0712261    1.058681
          L4event |   1.413904   .8031157     1.76   0.079    -.1640956    2.991903
          L5event |   2.288438   .5797184     3.95   0.000      1.14938    3.427496
          L6event |  -.2066322   .9128019    -0.23   0.821    -2.000148    1.586884
          L7event |  -.3179532   .7918114    -0.40   0.688    -1.873742    1.237835
          F1event |          0  (omitted)
        ln_ew_ges |   2.429389   1.342622     1.81   0.071    -.2086573    5.067436
         ew_biodt |   .3950984   .0300854    13.13   0.000     .3359852    .4542116
        ew_dtmihi |  -.1904761   .0614734    -3.10   0.002    -.3112619   -.0696904
         ew_ledig |   .2945637   .0755651     3.90   0.000     .1460897    .4430376
       ew_married |   .3322983   .0758445     4.38   0.000     .1832754    .4813211
        wb_anteil |  -.2530673   .0230274   -10.99   0.000    -.2983127   -.2078219
          wb_ausl |   -.071503    .015233    -4.69   0.000    -.1014337   -.0415724
         wb_18t24 |  -.0283633   .0301882    -0.94   0.348    -.0876784    .0309518
         wb_25t34 |   .0428903   .0201868     2.12   0.034     .0032263    .0825543
         wb_35t44 |  -.0223229   .0256594    -0.87   0.385    -.0727397    .0280938
         wb_45t59 |  -.0418609   .0213239    -1.96   0.050    -.0837591    .0000372
          avg_dur |   .0366065   .0242515     1.51   0.132     -.011044     .084257
          hh_kids |  -.1044675   .0458055    -2.28   0.023    -.1944684   -.0144667
mpreis_flats_rent |  -.0157752   .0268335    -0.59   0.557    -.0684989    .0369486
            _cons |  -18.93217   11.61282    -1.63   0.104    -41.74959    3.885245
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       488         488           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,872
Absorbing 2 HDFE groups                           F(  26,    483) =      17.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9628
                                                  Adj R-squared   =     0.9545
                                                  Within R-sq.    =     0.2303
Number of clusters (sb_new)  =        484         Root MSE        =     1.6442

                                    (Std. err. adjusted for 484 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |   .0850886   .3417216     0.25   0.803    -.5863559    .7565332
          F6event |   .3489038   .2721623     1.28   0.200    -.1858646    .8836721
          F5event |  -.6484663    .320482    -2.02   0.044    -1.278177   -.0187551
          F4event |  -.1964579   .2043802    -0.96   0.337    -.5980421    .2051263
          F3event |   .2396393   .1767007     1.36   0.176    -.1075577    .5868363
          F2event |   -.118307    .147885    -0.80   0.424    -.4088843    .1722704
          L0event |   .5821404   .2621503     2.22   0.027     .0670445    1.097236
          L1event |   1.088972   .2676427     4.07   0.000     .5630844     1.61486
          L2event |   1.233366   .3067239     4.02   0.000     .6306881    1.836044
          L3event |     .48008   .2906522     1.65   0.099    -.0910189    1.051179
          L4event |   3.052687   .6416393     4.76   0.000     1.791938    4.313436
          L5event |   2.895084   .7479142     3.87   0.000     1.425516    4.364651
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   2.422365   1.351986     1.79   0.074    -.2341362    5.078865
         ew_biodt |   .3978967   .0301887    13.18   0.000     .3385794    .4572141
        ew_dtmihi |  -.1949204   .0617248    -3.16   0.002    -.3162028    -.073638
         ew_ledig |    .287953    .075846     3.80   0.000     .1389241    .4369819
       ew_married |   .3243139   .0759889     4.27   0.000     .1750042    .4736237
        wb_anteil |  -.2553267     .02321   -11.00   0.000    -.3009317   -.2097217
          wb_ausl |  -.0696678   .0152605    -4.57   0.000    -.0996528   -.0396827
         wb_18t24 |  -.0300282   .0301289    -1.00   0.319    -.0892282    .0291718
         wb_25t34 |   .0433761   .0203609     2.13   0.034     .0033691     .083383
         wb_35t44 |  -.0230071   .0257199    -0.89   0.371    -.0735439    .0275297
         wb_45t59 |  -.0417815   .0213813    -1.95   0.051    -.0837934    .0002305
          avg_dur |   .0374659   .0243009     1.54   0.124    -.0102827    .0852144
          hh_kids |  -.0995826   .0457366    -2.18   0.030    -.1894498   -.0097154
mpreis_flats_rent |  -.0146693   .0269258    -0.54   0.586    -.0675755    .0382368
            _cons |  -18.33677   11.63196    -1.58   0.116    -41.19227    4.518735
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       484         484           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        146        146

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,552
Absorbing 2 HDFE groups                           F(  25,    443) =      19.27
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9633
                                                  Adj R-squared   =     0.9548
                                                  Within R-sq.    =     0.2467
Number of clusters (sb_new)  =        444         Root MSE        =     1.6362

                                    (Std. err. adjusted for 444 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |  -.3652898   .4300752    -0.85   0.396    -1.210531    .4799513
          F5event |  -.3436439   .2881531    -1.19   0.234    -.9099608    .2226729
          F4event |  -.0489295    .189923    -0.26   0.797    -.4221914    .3243325
          F3event |   .0896093   .2307072     0.39   0.698    -.3638073     .543026
          F2event |  -.1292713   .1673708    -0.77   0.440    -.4582108    .1996682
          L0event |    1.28928   .2880499     4.48   0.000     .7231662    1.855394
          L1event |   1.257927   .2862808     4.39   0.000     .6952902    1.820565
          L2event |   1.397342   .3264438     4.28   0.000     .7557706    2.038912
          L3event |   .5242927   .2982194     1.76   0.079    -.0618079    1.110393
          L4event |   3.264814   .6504212     5.02   0.000     1.986519    4.543108
          L5event |   3.059627   .7553772     4.05   0.000     1.575059    4.544195
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   2.183229   1.355252     1.61   0.108    -.4802938    4.846751
         ew_biodt |   .4131452   .0310115    13.32   0.000     .3521973    .4740931
        ew_dtmihi |  -.1748245   .0644971    -2.71   0.007    -.3015828   -.0480662
         ew_ledig |   .3031668   .0799503     3.79   0.000     .1460377    .4602959
       ew_married |   .3381775   .0792628     4.27   0.000     .1823996    .4939554
        wb_anteil |  -.2604603   .0237437   -10.97   0.000    -.3071246    -.213796
          wb_ausl |  -.0706019   .0160378    -4.40   0.000    -.1021216   -.0390822
         wb_18t24 |  -.0361241     .03314    -1.09   0.276    -.1012552     .029007
         wb_25t34 |   .0509102   .0218611     2.33   0.020     .0079458    .0938746
         wb_35t44 |  -.0246926   .0277789    -0.89   0.375    -.0792873    .0299022
         wb_45t59 |  -.0508388   .0218861    -2.32   0.021    -.0938524   -.0078253
          avg_dur |    .032447   .0245885     1.32   0.188    -.0158776    .0807715
          hh_kids |  -.0903521   .0488221    -1.85   0.065    -.1863037    .0055996
mpreis_flats_rent |  -.0161303   .0277085    -0.58   0.561    -.0705868    .0383262
            _cons |  -18.65755   12.04226    -1.55   0.122    -42.32462    5.009508
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       444         444           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        106        106

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  23,    437) =      20.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9629
                                                  Adj R-squared   =     0.9542
                                                  Within R-sq.    =     0.2451
Number of clusters (sb_new)  =        438         Root MSE        =     1.6398

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |   -.353238   .4306115    -0.82   0.412    -1.199565     .493089
          F5event |  -.3382077   .2884601    -1.17   0.242    -.9051493    .2287339
          F4event |  -.0582536   .1911728    -0.30   0.761    -.4339861    .3174789
          F3event |   .0747094   .2340562     0.32   0.750    -.3853064    .5347252
          F2event |  -.1529125   .1735202    -0.88   0.379    -.4939503    .1881253
          L0event |   1.347396   .2973178     4.53   0.000     .7630451    1.931746
          L1event |   1.321688   .2954494     4.47   0.000     .7410098    1.902367
          L2event |   1.375704   .3412662     4.03   0.000     .7049771    2.046431
          L3event |   .4054416   .3051962     1.33   0.185    -.1943932    1.005276
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   2.242542   1.367268     1.64   0.102    -.4446968    4.929781
         ew_biodt |   .4136537   .0311289    13.29   0.000     .3524727    .4748347
        ew_dtmihi |  -.1735243   .0647293    -2.68   0.008    -.3007439   -.0463048
         ew_ledig |    .309945   .0809101     3.83   0.000     .1509238    .4689662
       ew_married |   .3357426   .0799536     4.20   0.000     .1786012     .492884
        wb_anteil |  -.2591424   .0238544   -10.86   0.000     -.306026   -.2122589
          wb_ausl |  -.0707626   .0161124    -4.39   0.000      -.10243   -.0390951
         wb_18t24 |   -.037122   .0334575    -1.11   0.268    -.1028796    .0286356
         wb_25t34 |    .048105   .0219872     2.19   0.029     .0048912    .0913188
         wb_35t44 |  -.0254835   .0278206    -0.92   0.360    -.0801623    .0291953
         wb_45t59 |  -.0502739   .0220192    -2.28   0.023    -.0935505   -.0069972
          avg_dur |   .0324376   .0246578     1.32   0.189    -.0160251    .0809003
          hh_kids |  -.0917075   .0490351    -1.87   0.062    -.1880815    .0046664
mpreis_flats_rent |  -.0149069   .0277944    -0.54   0.592    -.0695342    .0397203
            _cons |  -19.43497   12.15361    -1.60   0.111    -43.32177    4.451832
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  24,    437) =      19.58
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9633
                                                  Adj R-squared   =     0.9547
                                                  Within R-sq.    =     0.2440
Number of clusters (sb_new)  =        438         Root MSE        =     1.6360

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |  -.3168275   .2763941    -1.15   0.252    -.8600546    .2263996
          F4event |  -.1697534   .1852093    -0.92   0.360    -.5337651    .1942584
          F3event |  -.0220118   .2376681    -0.09   0.926    -.4891264    .4451028
          F2event |  -.2005843   .1735799    -1.16   0.248    -.5417396    .1405709
          L0event |   1.254566    .303234     4.14   0.000     .6585874    1.850544
          L1event |   1.177711   .3057848     3.85   0.000      .576719    1.778702
          L2event |   1.305207   .3308296     3.95   0.000     .6549924    1.955422
          L3event |   .4560068   .3025478     1.51   0.132     -.138623    1.050637
          L4event |   3.194702   .6546005     4.88   0.000     1.908145    4.481259
          L5event |   3.002424   .7610586     3.95   0.000     1.506633    4.498214
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   2.134541   1.355439     1.57   0.116    -.5294486     4.79853
         ew_biodt |   .4083134   .0311659    13.10   0.000     .3470598    .4695671
        ew_dtmihi |  -.1738197    .065652    -2.65   0.008    -.3028527   -.0447867
         ew_ledig |    .307169   .0806706     3.81   0.000     .1486185    .4657196
       ew_married |   .3369444   .0803391     4.19   0.000     .1790454    .4948434
        wb_anteil |   -.256046   .0239252   -10.70   0.000    -.3030688   -.2090232
          wb_ausl |  -.0712542   .0161013    -4.43   0.000    -.1028997   -.0396087
         wb_18t24 |  -.0367052   .0337106    -1.09   0.277    -.1029602    .0295498
         wb_25t34 |   .0497826   .0220601     2.26   0.025     .0064255    .0931396
         wb_35t44 |   -.026236   .0279724    -0.94   0.349    -.0812131     .028741
         wb_45t59 |  -.0480803   .0221331    -2.17   0.030    -.0915807   -.0045798
          avg_dur |    .033924   .0251268     1.35   0.178    -.0154604    .0833083
          hh_kids |  -.0899951   .0515062    -1.75   0.081    -.1912258    .0112356
mpreis_flats_rent |  -.0169194    .028138    -0.60   0.548    -.0722221    .0383832
            _cons |   -18.4582   12.13023    -1.52   0.129    -42.29904    5.382637
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  22,    431) =      20.40
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9629
                                                  Adj R-squared   =     0.9542
                                                  Within R-sq.    =     0.2424
Number of clusters (sb_new)  =        432         Root MSE        =     1.6396

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |  -.3177852   .2783712    -1.14   0.254    -.8649192    .2293488
          F4event |  -.1846473   .1853606    -1.00   0.320    -.5489704    .1796757
          F3event |  -.0441353   .2410251    -0.18   0.855    -.5178662    .4295956
          F2event |  -.2318892   .1801612    -1.29   0.199     -.585993    .1222146
          L0event |   1.316149   .3145579     4.18   0.000      .697891    1.934408
          L1event |   1.243357   .3181669     3.91   0.000     .6180051    1.868709
          L2event |   1.276413   .3462644     3.69   0.000     .5958365     1.95699
          L3event |   .3309612   .3099105     1.07   0.286    -.2781626     .940085
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   2.196377   1.367522     1.61   0.109    -.4914638    4.884218
         ew_biodt |   .4087621   .0312801    13.07   0.000     .3472817    .4702425
        ew_dtmihi |  -.1724686     .06589    -2.62   0.009    -.3019743   -.0429628
         ew_ledig |   .3139282    .081657     3.84   0.000     .1534327    .4744237
       ew_married |   .3344284   .0810542     4.13   0.000     .1751178     .493739
        wb_anteil |  -.2547165   .0240391   -10.60   0.000     -.301965    -.207468
          wb_ausl |  -.0714395   .0161751    -4.42   0.000    -.1032313   -.0396477
         wb_18t24 |  -.0376454   .0340422    -1.11   0.269    -.1045548    .0292641
         wb_25t34 |   .0468778   .0221855     2.11   0.035     .0032725    .0904831
         wb_35t44 |  -.0270654    .028017    -0.97   0.335    -.0821322    .0280015
         wb_45t59 |  -.0476295   .0222632    -2.14   0.033    -.0913875   -.0038715
          avg_dur |   .0338776    .025203     1.34   0.180    -.0156585    .0834138
          hh_kids |   -.091501   .0517309    -1.77   0.078    -.1931772    .0101753
mpreis_flats_rent |  -.0158303     .02821    -0.56   0.575    -.0712765    .0396159
            _cons |  -19.23747   12.24348    -1.57   0.117    -43.30182    4.826886
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  28,    487) =      46.25
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9881
                                                  Within R-sq.    =     0.4491
Number of clusters (sb_new)  =        488         Root MSE        =     1.6205

                                    (Std. err. adjusted for 488 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1510082   .3481739    -0.43   0.665    -.8351167    .5331002
          F6event |   .3149134   .3038344     1.04   0.301    -.2820747    .9119015
          F5event |  -.4371194      .3384    -1.29   0.197    -1.102024    .2277847
          F4event |  -.1827598   .2101781    -0.87   0.385    -.5957276     .230208
          F3event |   .0462851   .1942401     0.24   0.812     -.335367    .4279372
          F2event |  -.1515435   .1748534    -0.87   0.387    -.4951038    .1920168
          L0event |   -.527548   .2090106    -2.52   0.012    -.9382218   -.1168742
          L1event |  -.1816749   .2446418    -0.74   0.458    -.6623586    .2990088
          L2event |   .3481369    .239119     1.46   0.146    -.1216954    .8179691
          L3event |   .0534333   .2623862     0.20   0.839    -.4621156    .5689821
          L4event |   .2054893   .7772849     0.26   0.792    -1.321757    1.732735
          L5event |   1.419982   .7097771     2.00   0.046     .0253781    2.814585
          L6event |   .5968236   .7782912     0.77   0.444    -.9323997    2.126047
          L7event |   .2826843   1.095866     0.26   0.797    -1.870526    2.435894
          F1event |          0  (omitted)
        ln_ew_ges |   .7918354   1.004715     0.79   0.431    -1.182275    2.765946
         ew_biodt |   .7584086   .0324441    23.38   0.000     .6946609    .8221563
        ew_dtmihi |  -.1559771   .0538801    -2.89   0.004    -.2618432    -.050111
         ew_ledig |   .4778891   .0675408     7.08   0.000     .3451817    .6105965
       ew_married |   .6965607   .0682274    10.21   0.000     .5625043     .830617
        wb_anteil |  -.5340446   .0244271   -21.86   0.000    -.5820402   -.4860491
          wb_ausl |  -.0407825   .0198957    -2.05   0.041    -.0798745   -.0016905
         wb_18t24 |  -.0404345   .0272545    -1.48   0.139    -.0939854    .0131164
         wb_25t34 |  -.0175762   .0182633    -0.96   0.336    -.0534608    .0183085
         wb_35t44 |  -.0236185   .0224757    -1.05   0.294    -.0677798    .0205428
         wb_45t59 |  -.0179228   .0206735    -0.87   0.386    -.0585431    .0226976
          avg_dur |   .0000374   .0240645     0.00   0.999    -.0472457    .0473204
          hh_kids |  -.0985307   .0385277    -2.56   0.011    -.1742317   -.0228297
mpreis_flats_rent |   .0175361   .0263756     0.66   0.506    -.0342879    .0693601
            _cons |   1.932135   9.913442     0.19   0.846    -17.54626    21.41053
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       488         488           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,872
Absorbing 2 HDFE groups                           F(  26,    483) =      49.28
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9881
                                                  Within R-sq.    =     0.4498
Number of clusters (sb_new)  =        484         Root MSE        =     1.6209

                                    (Std. err. adjusted for 484 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |  -.1466328   .3482171    -0.42   0.674    -.8308403    .5375747
          F6event |   .3204278   .3039995     1.05   0.292    -.2768971    .9177527
          F5event |  -.4358568   .3391816    -1.29   0.199    -1.102311    .2305969
          F4event |  -.1805814   .2112139    -0.85   0.393    -.5955929    .2344301
          F3event |   .0453313   .1951842     0.23   0.816    -.3381837    .4288463
          F2event |  -.1523309   .1765744    -0.86   0.389    -.4992798     .194618
          L0event |  -.5638544    .209442    -2.69   0.007    -.9753843   -.1523245
          L1event |  -.2208727   .2489051    -0.89   0.375    -.7099433    .2681978
          L2event |   .3736264   .2423759     1.54   0.124     -.102615    .8498678
          L3event |    .047781    .269296     0.18   0.859    -.4813554    .5769173
          L4event |   1.530957   .5734672     2.67   0.008     .4041588    2.657756
          L5event |   1.925629   .9960113     1.93   0.054    -.0314214    3.882679
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   .8089399   1.008798     0.80   0.423    -1.173234    2.791114
         ew_biodt |   .7597191   .0325455    23.34   0.000     .6957709    .8236672
        ew_dtmihi |  -.1627896   .0539836    -3.02   0.003    -.2688613   -.0567179
         ew_ledig |   .4679735   .0673167     6.95   0.000     .3357037    .6002434
       ew_married |   .6818317   .0676924    10.07   0.000     .5488238    .8148397
        wb_anteil |  -.5325466   .0246056   -21.64   0.000    -.5808938   -.4841994
          wb_ausl |  -.0400147   .0199505    -2.01   0.045    -.0792152   -.0008142
         wb_18t24 |  -.0415781   .0272149    -1.53   0.127    -.0950524    .0118962
         wb_25t34 |  -.0174892   .0184222    -0.95   0.343    -.0536867    .0187083
         wb_35t44 |  -.0235101   .0224571    -1.05   0.296    -.0676358    .0206156
         wb_45t59 |  -.0178809   .0206767    -0.86   0.388    -.0585082    .0227464
          avg_dur |   .0004087    .024178     0.02   0.987    -.0470983    .0479157
          hh_kids |  -.0927483   .0383249    -2.42   0.016    -.1680524   -.0174441
mpreis_flats_rent |   .0163784   .0265148     0.62   0.537    -.0357202    .0684771
            _cons |   2.671083   9.913269     0.27   0.788    -16.80738    22.14954
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       484         484           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        146        146

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,552
Absorbing 2 HDFE groups                           F(  25,    443) =      48.91
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9902
                                                  Adj R-squared   =     0.9880
                                                  Within R-sq.    =     0.4588
Number of clusters (sb_new)  =        444         Root MSE        =     1.6306

                                    (Std. err. adjusted for 444 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |   .3671099   .6722088     0.55   0.585    -.9540045    1.688224
          F5event |   .4266583   .3774926     1.13   0.259    -.3152406    1.168557
          F4event |  -.0468496   .2367082    -0.20   0.843    -.5120602     .418361
          F3event |  -.0039117   .2379706    -0.02   0.987    -.4716034    .4637799
          F2event |    .001813   .2132491     0.01   0.993    -.4172927    .4209186
          L0event |  -.2404691   .2475837    -0.97   0.332    -.7270536    .2461154
          L1event |  -.0870994   .2719843    -0.32   0.749    -.6216392    .4474404
          L2event |   .5272671   .2651936     1.99   0.047     .0060733    1.048461
          L3event |   .1245107   .2831888     0.44   0.660    -.4320497    .6810712
          L4event |   1.730558   .5848116     2.96   0.003     .5812079    2.879908
          L5event |   2.086261     1.0083     2.07   0.039     .1046152    4.067906
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   1.119147    1.05977     1.06   0.292    -.9636552    3.201949
         ew_biodt |   .7808928   .0339884    22.98   0.000     .7140944    .8476913
        ew_dtmihi |  -.1556971   .0559228    -2.78   0.006     -.265604   -.0457903
         ew_ledig |   .4898779   .0708748     6.91   0.000     .3505854    .6291704
       ew_married |    .695408    .070247     9.90   0.000     .5573491    .8334668
        wb_anteil |  -.5391734    .025758   -20.93   0.000    -.5897964   -.4885504
          wb_ausl |  -.0392547   .0209319    -1.88   0.061    -.0803929    .0018835
         wb_18t24 |  -.0478046   .0290545    -1.65   0.101    -.1049064    .0092972
         wb_25t34 |  -.0132547   .0197481    -0.67   0.502    -.0520663     .025557
         wb_35t44 |  -.0238933    .024219    -0.99   0.324    -.0714917     .023705
         wb_45t59 |  -.0200434   .0217076    -0.92   0.356    -.0627061    .0226193
          avg_dur |   .0008298   .0248555     0.03   0.973    -.0480195    .0496791
          hh_kids |  -.0989127   .0401448    -2.46   0.014    -.1778107   -.0200148
mpreis_flats_rent |   .0252173   .0273987     0.92   0.358    -.0286302    .0790648
            _cons |  -2.415758   10.41803    -0.23   0.817    -22.89066    18.05914
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       444         444           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        106        106

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  23,    437) =      52.78
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9902
                                                  Adj R-squared   =     0.9879
                                                  Within R-sq.    =     0.4589
Number of clusters (sb_new)  =        438         Root MSE        =     1.6360

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |   .3221541   .6740137     0.48   0.633    -1.002557    1.646865
          F5event |   .3868472   .3817038     1.01   0.311    -.3633563    1.137051
          F4event |  -.0936937   .2433603    -0.39   0.700    -.5719958    .3846083
          F3event |  -.0533763   .2476518    -0.22   0.829     -.540113    .4333604
          F2event |  -.0083151   .2247364    -0.04   0.971    -.4500137    .4333835
          L0event |  -.3215922   .2617504    -1.23   0.220    -.8360384     .192854
          L1event |  -.1329825   .2863794    -0.46   0.643    -.6958347    .4298697
          L2event |   .5142758   .2773314     1.85   0.064    -.0307933    1.059345
          L3event |   .0431469    .297816     0.14   0.885    -.5421829    .6284767
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   1.190326   1.068792     1.11   0.266     -.910286    3.290938
         ew_biodt |   .7833995   .0341606    22.93   0.000       .71626     .850539
        ew_dtmihi |  -.1564587   .0561312    -2.79   0.006    -.2667794    -.046138
         ew_ledig |   .4850009   .0714313     6.79   0.000     .3446094    .6253924
       ew_married |   .6942004   .0709522     9.78   0.000     .5547505    .8336503
        wb_anteil |  -.5389776   .0258672   -20.84   0.000    -.5898172   -.4881379
          wb_ausl |  -.0384083     .02099    -1.83   0.068    -.0796622    .0028456
         wb_18t24 |  -.0441391   .0294034    -1.50   0.134    -.1019287    .0136506
         wb_25t34 |  -.0142075   .0199158    -0.71   0.476    -.0533502    .0249351
         wb_35t44 |  -.0225393   .0242513    -0.93   0.353    -.0702029    .0251244
         wb_45t59 |   -.018116   .0218119    -0.83   0.407    -.0609853    .0247533
          avg_dur |    .000688   .0248998     0.03   0.978    -.0482503    .0496263
          hh_kids |  -.0984011   .0403186    -2.44   0.015    -.1776435   -.0191587
mpreis_flats_rent |   .0267478   .0275814     0.97   0.333    -.0274608    .0809565
            _cons |  -2.986054   10.51717    -0.28   0.777    -23.65658    17.68447
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  24,    437) =      49.64
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9902
                                                  Adj R-squared   =     0.9880
                                                  Within R-sq.    =     0.4568
Number of clusters (sb_new)  =        438         Root MSE        =     1.6318

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |   .4242099   .4404608     0.96   0.336    -.4414749    1.289895
          F4event |  -.0969851   .2391381    -0.41   0.685    -.5669888    .3730185
          F3event |   .0268464    .256456     0.10   0.917    -.4771941    .5308869
          F2event |   -.027859   .2281952    -0.12   0.903    -.4763556    .4206376
          L0event |  -.1692542   .2619991    -0.65   0.519    -.6841892    .3456809
          L1event |  -.1119906    .290314    -0.39   0.700    -.6825758    .4585946
          L2event |   .4903452   .2728062     1.80   0.073    -.0458302    1.026521
          L3event |   .1352145    .290067     0.47   0.641    -.4348852    .7053143
          L4event |   1.704128   .5872317     2.90   0.004      .549979    2.858278
          L5event |   2.084989   1.008355     2.07   0.039     .1031606    4.066818
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |    1.07763   1.055751     1.02   0.308    -.9973503    3.152611
         ew_biodt |   .7753572   .0343077    22.60   0.000     .7079286    .8427858
        ew_dtmihi |  -.1554328   .0567839    -2.74   0.006    -.2670362   -.0438294
         ew_ledig |    .495261   .0714631     6.93   0.000     .3548068    .6357152
       ew_married |   .6918679   .0708905     9.76   0.000     .5525391    .8311966
        wb_anteil |  -.5333018   .0258663   -20.62   0.000    -.5841397   -.4824639
          wb_ausl |  -.0417577   .0212852    -1.96   0.050    -.0835918    .0000763
         wb_18t24 |  -.0495584   .0295344    -1.68   0.094    -.1076054    .0084887
         wb_25t34 |  -.0156713   .0198876    -0.79   0.431    -.0547586     .023416
         wb_35t44 |  -.0230757   .0243273    -0.95   0.343    -.0708888    .0247374
         wb_45t59 |  -.0218799   .0219289    -1.00   0.319    -.0649791    .0212192
          avg_dur |   .0034144   .0250803     0.14   0.892    -.0458786    .0527074
          hh_kids |  -.0976052   .0421295    -2.32   0.021    -.1804068   -.0148036
mpreis_flats_rent |   .0233755   .0276526     0.85   0.398    -.0309731    .0777241
            _cons |    -2.2365   10.40966    -0.21   0.830    -22.69572    18.22272
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event omitted because of collinearity
note: F6event omitted because of collinearity
note: L4event omitted because of collinearity
note: L5event omitted because of collinearity
note: L6event omitted because of collinearity
note: L7event omitted because of collinearity
note: F1event omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  22,    431) =      53.71
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9902
                                                  Adj R-squared   =     0.9879
                                                  Within R-sq.    =     0.4568
Number of clusters (sb_new)  =        432         Root MSE        =     1.6375

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
          F7event |          0  (omitted)
          F6event |          0  (omitted)
          F5event |   .3844237   .4442074     0.87   0.387    -.4886585    1.257506
          F4event |  -.1478234    .246982    -0.60   0.550    -.6332624    .3376156
          F3event |   -.027034   .2683297    -0.10   0.920    -.5544316    .5003636
          F2event |  -.0404714   .2418723    -0.17   0.867    -.5158674    .4349246
          L0event |  -.2518697   .2797461    -0.90   0.368    -.8017059    .2979666
          L1event |  -.1637918   .3079028    -0.53   0.595    -.7689696     .441386
          L2event |    .471916    .286319     1.65   0.100    -.0908392    1.034671
          L3event |   .0519235   .3061151     0.17   0.865    -.5497407    .6535878
          L4event |          0  (omitted)
          L5event |          0  (omitted)
          L6event |          0  (omitted)
          L7event |          0  (omitted)
          F1event |          0  (omitted)
        ln_ew_ges |   1.149485    1.06457     1.08   0.281    -.9429108     3.24188
         ew_biodt |   .7780234   .0344847    22.56   0.000     .7102443    .8458025
        ew_dtmihi |   -.155998   .0569907    -2.74   0.006    -.2680123   -.0439838
         ew_ledig |    .490452   .0720319     6.81   0.000     .3488745    .6320295
       ew_married |   .6906151   .0716153     9.64   0.000     .5498564    .8313738
        wb_anteil |  -.5332059   .0259817   -20.52   0.000    -.5842725   -.4821392
          wb_ausl |  -.0408679   .0213478    -1.91   0.056    -.0828267     .001091
         wb_18t24 |   -.045997   .0298981    -1.54   0.125    -.1047612    .0127672
         wb_25t34 |  -.0166158   .0200613    -0.83   0.408     -.056046    .0228143
         wb_35t44 |  -.0216666   .0243587    -0.89   0.374    -.0695433      .02621
         wb_45t59 |  -.0198458   .0220381    -0.90   0.368    -.0631614    .0234697
          avg_dur |   .0032788    .025126     0.13   0.896    -.0461059    .0526635
          hh_kids |  -.0972835   .0423164    -2.30   0.022    -.1804558   -.0141113
mpreis_flats_rent |    .024928   .0278387     0.90   0.371    -.0297886    .0796445
            _cons |   -2.81942   10.50688    -0.27   0.789    -23.47053    17.83168
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

.         
.         * TABLE E8. Balanced Sample Results–Pooled Reassignments
.         outreg using "$tables/Table_E8_ES_bal_samples_bsl", replay tex replace fragment
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_E8
> _ES_bal_samples_bsl.tex not found)
                ------------------------------------------------------------------
                                                  (1)        (2)         (3)     
                                                                                 
                ------------------------------------------------------------------
                 Reassignment (#t-4#)             0.03       0.02        0.00    
                                                 (0.21)     (0.21)      (0.23)   
                 Reassignment (#t-3#)            -0.18      -0.19       -0.09    
                                                 (0.21)     (0.21)      (0.24)   
                 Reassignment (#t-2#)            -0.04      -0.03        0.13    
                                                 (0.17)     (0.17)      (0.20)   
                 Reassignment (#t+0#)           -1.14***   -1.15***    -1.53***  
                                                 (0.28)     (0.28)      (0.33)   
                 Reassignment (#t+1#)           -1.32***   -1.31***    -1.35***  
                                                 (0.28)     (0.28)      (0.30)   
                 Reassignment (#t+2#)           -0.84**    -0.86**     -0.87**   
                                                 (0.29)     (0.30)      (0.32)   
                 R2                               0.97       0.97        0.97    
                 N                               3,904      3,872       3,552    
                 Unbalanced sample                 X                             
                 #treated precincts               150        146         106     
                 #control precincts               338        338         338     
                 Balanced sample, $ \tau \in$             $ [-4,0] $  $ [-2,1] $ 
                ------------------------------------------------------------------


     ---------------------------------------------------------------------------------------
                                                        (4)                        (5)     
                                     \multicolumn{6}{c}{Polling Place Turnout}             
     ---------------------------------------------------------------------------------------
      Reassignment (#t-4#)                             -0.04                       0.07    
                                                      (0.23)                      (0.24)   
      Reassignment (#t-3#)                             -0.13                       0.05    
                                                      (0.26)                      (0.25)   
      Reassignment (#t-2#)                             0.14                        0.17    
                                                      (0.21)                      (0.20)   
      Reassignment (#t+0#)                           -1.67***                    -1.42***  
                                                      (0.34)                      (0.35)   
      Reassignment (#t+1#)                           -1.45***                    -1.29***  
                                                      (0.32)                      (0.32)   
      Reassignment (#t+2#)                            -0.86*                      -0.81*   
                                                      (0.33)                      (0.32)   
      R2                                               0.97                        0.97    
      N                                                3,504                      3,504    
      Unbalanced sample                                                                    
      #treated precincts                               100                         100     
      #control precincts                                338                        338     
      Balanced sample, $ \tau \in$                  $ [-4,1] $                  $ [-2,2] $ 
     ---------------------------------------------------------------------------------------


          -----------------------------------------------------------------------------
                                             (6)        (7)       (8)         (9)     
                                                                                      
          -----------------------------------------------------------------------------
           Reassignment (#t-4#)              0.04      -0.21     -0.20       -0.05    
                                            (0.25)    (0.20)     (0.20)      (0.19)   
           Reassignment (#t-3#)              0.02      0.22       0.24        0.09    
                                            (0.26)    (0.18)     (0.18)      (0.23)   
           Reassignment (#t-2#)              0.19      -0.11     -0.12       -0.13    
                                            (0.21)    (0.15)     (0.15)      (0.17)   
           Reassignment (#t+0#)            -1.57***    0.61*     0.58*      1.29***   
                                            (0.37)    (0.26)     (0.26)      (0.29)   
           Reassignment (#t+1#)            -1.41***   1.14***   1.09***     1.26***   
                                            (0.34)    (0.27)     (0.27)      (0.29)   
           Reassignment (#t+2#)             -0.80*    1.18***   1.23***     1.40***   
                                            (0.34)    (0.30)     (0.31)      (0.33)   
           R2                                0.97      0.96       0.96        0.96    
           N                                3,456      3,904     3,872       3,552    
           Unbalanced sample                            X                             
           #treated precincts                94        150        146         106     
           #control precincts                338        338       338         338     
           Balanced sample, $ \tau \in$   $ [-4,2] $           $ [-4,0] $  $ [-2,1] $ 
          -----------------------------------------------------------------------------


  ---------------------------------------------------------------------------------------------
                                                 (10)                     (11)        (12)    
                                  \multicolumn{6}{c}{Mail-in turnout}                         
  ---------------------------------------------------------------------------------------------
   Reassignment (#t-4#)                          -0.06                   -0.17       -0.18    
                                                (0.19)                   (0.19)      (0.19)   
   Reassignment (#t-3#)                          0.07                    -0.02       -0.04    
                                                (0.23)                   (0.24)      (0.24)   
   Reassignment (#t-2#)                          -0.15                   -0.20       -0.23    
                                                (0.17)                   (0.17)      (0.18)   
   Reassignment (#t+0#)                         1.35***                 1.25***     1.32***   
                                                (0.30)                   (0.30)      (0.31)   
   Reassignment (#t+1#)                         1.32***                 1.18***     1.24***   
                                                (0.30)                   (0.31)      (0.32)   
   Reassignment (#t+2#)                         1.38***                 1.31***     1.28***   
                                                (0.34)                   (0.33)      (0.35)   
   R2                                            0.96                     0.96        0.96    
   N                                             3,504                   3,504       3,456    
   Unbalanced sample                                                                          
   #treated precincts                            100                      100         94      
   #control precincts                             338                     338         338     
   Balanced sample, $ \tau \in$               $ [-4,1] $               $ [-2,2] $  $ [-4,2] $ 
  ---------------------------------------------------------------------------------------------


                 ----------------------------------------------------------------
                                                  (13)      (14)        (15)    
                                                                                
                 ----------------------------------------------------------------
                  Reassignment (#t-4#)           -0.18     -0.18       -0.05    
                                                 (0.21)    (0.21)      (0.24)   
                  Reassignment (#t-3#)            0.05      0.05       -0.00    
                                                 (0.19)    (0.20)      (0.24)   
                  Reassignment (#t-2#)           -0.15     -0.15        0.00    
                                                 (0.17)    (0.18)      (0.21)   
                  Reassignment (#t+0#)           -0.53*   -0.56**      -0.24    
                                                 (0.21)    (0.21)      (0.25)   
                  Reassignment (#t+1#)           -0.18     -0.22       -0.09    
                                                 (0.24)    (0.25)      (0.27)   
                  Reassignment (#t+2#)            0.35      0.37       0.53*    
                                                 (0.24)    (0.24)      (0.27)   
                  R2                              0.99      0.99        0.99    
                  N                              3,904     3,872       3,552    
                  Unbalanced sample                X                            
                  #treated precincts              150       146         106     
                  #control precincts              338       338         338     
                  Balanced sample, $ \tau \in$           $ [-4,0] $  $ [-2,1] $ 
                 ----------------------------------------------------------------


   -------------------------------------------------------------------------------------------
                                                 (16)                    (17)        (18)    
                                   \multicolumn{6}{c}{Total turnout}                         
   -------------------------------------------------------------------------------------------
    Reassignment (#t-4#)                         -0.09                  -0.10       -0.15    
                                                (0.24)                  (0.24)      (0.25)   
    Reassignment (#t-3#)                         -0.05                   0.03       -0.03    
                                                (0.25)                  (0.26)      (0.27)   
    Reassignment (#t-2#)                         -0.01                  -0.03       -0.04    
                                                (0.22)                  (0.23)      (0.24)   
    Reassignment (#t+0#)                         -0.32                  -0.17       -0.25    
                                                (0.26)                  (0.26)      (0.28)   
    Reassignment (#t+1#)                         -0.13                  -0.11       -0.16    
                                                (0.29)                  (0.29)      (0.31)   
    Reassignment (#t+2#)                         0.51                    0.49        0.47    
                                                (0.28)                  (0.27)      (0.29)   
    R2                                           0.99                    0.99        0.99    
    N                                            3,504                  3,504       3,456    
    Unbalanced sample                                                                        
    #treated precincts                           100                     100         94      
    #control precincts                            338                    338         338     
    Balanced sample, $ \tau \in$              $ [-4,1] $              $ [-2,2] $  $ [-4,2] $ 
   -------------------------------------------------------------------------------------------


.         cleantex "$tables/Table_E8_ES_bal_samples_bsl.tex"  ,  replace  

\begin{tabular}{lcccccccccccccccccc}
\toprule  & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) & (14)
>  & (15) & (16) & (17) & (18)\\
 &  &  &  & \multicolumn{6}{c}{Polling Place Turnout} &  &  &  &  &  & \multicolumn{6}{c}{Mail-in 
> turnout} &  &  &  &  &  & \multicolumn{6}{c}{Total turnout} &  & \\
 Reassignment ($ t-4$) & 0.03 & 0.02 & 0.00 & -0.04 & 0.07 & 0.04 & -0.21 & -0.20 & -0.05 & -0.06 
> & -0.17 & -0.18 & -0.18 & -0.18 & -0.05 & -0.09 & -0.10 & -0.15\\
 & (0.21) & (0.21) & (0.23) & (0.23) & (0.24) & (0.25) & (0.20) & (0.20) & (0.19) & (0.19) & (0.19
> ) & (0.19) & (0.21) & (0.21) & (0.24) & (0.24) & (0.24) & (0.25)\\
Reassignment ($ t-3$) & -0.18 & -0.19 & -0.09 & -0.13 & 0.05 & 0.02 & 0.22 & 0.24 & 0.09 & 0.07 & 
> -0.02 & -0.04 & 0.05 & 0.05 & -0.00 & -0.05 & 0.03 & -0.03\\
 & (0.21) & (0.21) & (0.24) & (0.26) & (0.25) & (0.26) & (0.18) & (0.18) & (0.23) & (0.23) & (0.24
> ) & (0.24) & (0.19) & (0.20) & (0.24) & (0.25) & (0.26) & (0.27)\\
Reassignment ($ t-2$) & -0.04 & -0.03 & 0.13 & 0.14 & 0.17 & 0.19 & -0.11 & -0.12 & -0.13 & -0.15 
> & -0.20 & -0.23 & -0.15 & -0.15 & 0.00 & -0.01 & -0.03 & -0.04\\
 & (0.17) & (0.17) & (0.20) & (0.21) & (0.20) & (0.21) & (0.15) & (0.15) & (0.17) & (0.17) & (0.17
> ) & (0.18) & (0.17) & (0.18) & (0.21) & (0.22) & (0.23) & (0.24)\\
Reassignment ($ t+0$) & -1.14*** & -1.15*** & -1.53*** & -1.67*** & -1.42*** & -1.57*** & 0.61* & 
> 0.58* & 1.29*** & 1.35*** & 1.25*** & 1.32*** & -0.53* & -0.56** & -0.24 & -0.32 & -0.17 & -0.25
> \\
 & (0.28) & (0.28) & (0.33) & (0.34) & (0.35) & (0.37) & (0.26) & (0.26) & (0.29) & (0.30) & (0.30
> ) & (0.31) & (0.21) & (0.21) & (0.25) & (0.26) & (0.26) & (0.28)\\
Reassignment ($ t+1$) & -1.32*** & -1.31*** & -1.35*** & -1.45*** & -1.29*** & -1.41*** & 1.14*** 
> & 1.09*** & 1.26*** & 1.32*** & 1.18*** & 1.24*** & -0.18 & -0.22 & -0.09 & -0.13 & -0.11 & -0.1
> 6\\
 & (0.28) & (0.28) & (0.30) & (0.32) & (0.32) & (0.34) & (0.27) & (0.27) & (0.29) & (0.30) & (0.31
> ) & (0.32) & (0.24) & (0.25) & (0.27) & (0.29) & (0.29) & (0.31)\\
Reassignment ($ t+2$) & -0.84** & -0.86** & -0.87** & -0.86* & -0.81* & -0.80* & 1.18*** & 1.23***
>  & 1.40*** & 1.38*** & 1.31*** & 1.28*** & 0.35 & 0.37 & 0.53* & 0.51 & 0.49 & 0.47\\
 & (0.29) & (0.30) & (0.32) & (0.33) & (0.32) & (0.34) & (0.30) & (0.31) & (0.33) & (0.34) & (0.33
> ) & (0.35) & (0.24) & (0.24) & (0.27) & (0.28) & (0.27) & (0.29)\\
$ R^2$  & 0.97 & 0.97 & 0.97 & 0.97 & 0.97 & 0.97 & 0.96 & 0.96 & 0.96 & 0.96 & 0.96 & 0.96 & 0.99
>  & 0.99 & 0.99 & 0.99 & 0.99 & 0.99\\
Observations & 3,904 & 3,872 & 3,552 & 3,504 & 3,504 & 3,456 & 3,904 & 3,872 & 3,552 & 3,504 & 3,5
> 04 & 3,456 & 3,904 & 3,872 & 3,552 & 3,504 & 3,504 & 3,456\\
Unbalanced sample & $\times$  &  &  &  &  &  & $\times$  &  &  &  &  &  & $\times$  &  &  &  &  & 
> \\
\#treated precincts & 150  & 146  & 106  & 100  & 100  & 94  & 150  & 146  & 106  & 100  & 100  & 
> 94  & 150  & 146  & 106  & 100  & 100  & 94 \\
\#control precincts & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 
> & 338 & 338 & 338 & 338 & 338\\
Balanced sample, $ \tau \in$ &  & $ [-4,0] $ & $ [-2,1] $ & $ [-4,1] $ & $ [-2,2] $ & $ [-4,2] $ &
>   & $ [-4,0] $ & $ [-2,1] $ & $ [-4,1] $ & $ [-2,2] $ & $ [-4,2] $ &  & $ [-4,0] $ & $ [-2,1] $ 
> & $ [-4,1] $ & $ [-2,2] $ & $ [-4,2] $\\
\bottomrule\end{tabular}


.         
.         
. 
. *********************************************************************************
.         // Balanced estimates for heterogeneity by distance increase/ decrease //
. *********************************************************************************       
. 
.         // gen leads and lags
.         cap drop L* F*

.         forvalues l = 7(-1)1 {
  2.                 gen F`l'event = K==-`l'
  3.         }       

.         forvalues l = 0/7 {
  2.                 gen L`l'event = K==`l'
  3.         }

.         // Create two set of dummies: Reason Dummy x event-time dummies
.         forvalues l = 7(-1)1 {          
  2.                 gen     F`l'event_a = F`l'event *ind_dist_dn                            // a 
> := decrease
  3.                 lab var F`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t-`l'#)"
  4.                 gen             F`l'event_b = F`l'event *ind_dist_up                         
>    // b:= increase
  5.                 lab var F`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t-`l'#)"
  6. 
.                 assert  F`l'event_b+F`l'event_a==F`l'event              
  7.         }

.         forvalues l = 0/7 {             
  2.                 gen     L`l'event_a = L`l'event *ind_dist_dn    // a := decrease
  3.                 lab var L`l'event_a "(N-)x\hspace{.7cm}Reassignment (#t+`l'#)"
  4.                 gen             L`l'event_b = L`l'event *ind_dist_up    // b:= increase
  5.                 lab var L`l'event_b "(N+)x\hspace{.7cm}Reassignment (#t+`l'#)"
  6.                 assert  L`l'event_b+L`l'event_a==L`l'event      
  7.         }

.         // ORDER dummies
.         order *event_b, last

.         order F1event*,last             

.         
.         local smplif "& fulltottreat100<=1"

.         
.         estimates clear 

.         outreg, clear

.         
.         local j=0

.         foreach v of varlist turnout_urne turnout_pos_req turnout_tot_req {
  2.                 
.         // Basaeline (unbalanced)
.         local `++j'
  3.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b  $ct
> r $wgt ///
>                                 if fulltottreat100<=1 ,         absorb(i.wahl_id#i.stadtbez i.sb
> _new) cluster(sb_new)
  4.                 
.                 estimates store `v'_bsl 
  5.                 extract_N 100
  6.                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b)  ///
>                         addrow(Unbalanced sample, X \ Balanced sample, \ #treated precincts, `r(
> N_T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')")
  7.                 
.         // Balanced around -4/0 
.         local `++j'
  8.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b  $ct
> r $wgt ///
>                                 if smpl_bal_tp0==1 `smplif',    absorb(i.wahl_id#i.stadtbez i.sb
> _new) cluster(sb_new)
  9. 
.                 estimates store `v'_tp0 
 10.                 extract_N 100
 11.                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b)  ///
>                         addrow(Balanced sample, "$ \tau \in [-4,0] $" \ #treated precincts, `r(N
> _T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')")
 12.                 
.         // Balanced around -2/+1
.         local `++j'
 13.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b $ctr
>  $wgt ///
>                                         if  smpl_bal_tpm==1 `smplif', absorb(i.wahl_id#i.stadtbe
> z i.sb_new) cluster(sb_new)
 14. 
.                 estimates store `v'_tpm 
 15.                 extract_N 100
 16.                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b)  ///
>                         addrow(Balanced sample, "$ \tau \in [-2,1] $" \ #treated precincts, `r(N
> _T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')")
 17.                         
.         // Balanced around -4/+1
.         local `++j'
 18.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b $ctr
>  $wgt ///
>                                         if  smpl_bal_tp1==1 `smplif' , absorb(i.wahl_id#i.stadtb
> ez i.sb_new) cluster(sb_new)
 19. 
.                 estimates store `v'_tp1
 20.                 extract_N 100
 21.                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b)  ///
>                         addrow(Balanced sample, "$ \tau \in [-4,1] $" \ #treated precincts, `r(N
> _T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')" \"", "`:var lab `v''")
 22.         
.         // Balanced around -2/+2
.         local `++j'
 23.                 qui reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b 
> $ctr $wgt ///
>                                         if  smpl_bal_tm2==1 `smplif', absorb(i.wahl_id#i.stadtbe
> z i.sb_new) cluster(sb_new)
 24. 
.                 estimates store `v'_tm2 
 25.                 extract_N 100
 26.                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b)  ///
>                         addrow(Balanced sample, "$ \tau \in [-2,2] $" \ #treated precincts, `r(N
> _T)' \ #control precincts, `r(N_C)') ctitle("", "(`j')")
 27.                 
.                 
.         // Balanced around -4/+2
.         local `++j'
 28.                 reghdfe `v' F7event_a-L7event_a  F7event_b-L7event_b F1event_a F1event_b  $ct
> r $wgt ///
>                                 if smpl_bal==1 `smplif' , absorb(i.wahl_id#i.stadtbez i.sb_new) 
> cluster(sb_new)
 29. 
.                 estimates store `v'_bal
 30.                 extract_N 100
 31.                 qui outreg,  $opt  keep(F4event_a-L2event_a  F4event_b-L2event_b)  ///
>                         addrow(Balanced sample, "$ \tau \in [-4,2] $" \#treated precincts, `r(N_
> T)' \ #control precincts, `r(N_C)')     ctitle("", "(`j')")
 32.         }       
(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  42,    487) =      12.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9751
                                                  Adj R-squared   =     0.9694
                                                  Within R-sq.    =     0.2006
Number of clusters (sb_new)  =        488         Root MSE        =     1.6215

                                    (Std. err. adjusted for 488 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .3031671   .5954237     0.51   0.611    -.8667495    1.473084
        F6event_a |   -.067569   .6050292    -0.11   0.911    -1.256359    1.121221
        F5event_a |   .2320729   .4494721     0.52   0.606    -.6510711    1.115217
        F4event_a |  -.1765067   .2996989    -0.59   0.556    -.7653692    .4123559
        F3event_a |  -.2636339    .289679    -0.91   0.363    -.8328088     .305541
        F2event_a |  -.2590296    .232755    -1.11   0.266    -.7163576    .1982983
        L0event_a |   .3082375   .3743895     0.82   0.411    -.4273805    1.043856
        L1event_a |    .059568   .3779013     0.16   0.875    -.6829502    .8020862
        L2event_a |   .4565355   .3834137     1.19   0.234    -.2968139    1.209885
        L3event_a |   .5546618    .384739     1.44   0.150    -.2012915    1.310615
        L4event_a |  -.0090903   .8198093    -0.01   0.991     -1.61989     1.60171
        L5event_a |   .4255668   1.033054     0.41   0.681    -1.604226    2.455359
        L6event_a |   2.888935   1.189218     2.43   0.015     .5523035    5.225567
        L7event_a |   .1215772   .5948655     0.20   0.838    -1.047243    1.290397
        F7event_b |  -.5456228   .4136343    -1.32   0.188    -1.358351    .2671054
        F6event_b |  -.0169305   .3597138    -0.05   0.962    -.7237131    .6898522
        F5event_b |   .1557485   .3411879     0.46   0.648    -.5146335    .8261306
        F4event_b |   .1579521   .2639641     0.60   0.550    -.3606969    .6766012
        F3event_b |  -.1247117   .2658062    -0.47   0.639    -.6469804    .3975569
        F2event_b |   .1057368   .2101811     0.50   0.615    -.3072369    .5187105
        L0event_b |  -2.097294   .3355179    -6.25   0.000    -2.756536   -1.438053
        L1event_b |  -2.251805   .3303521    -6.82   0.000    -2.900896   -1.602713
        L2event_b |  -1.684986   .3629153    -4.64   0.000    -2.398059   -.9719131
        L3event_b |  -1.131406   .3578734    -3.16   0.002    -1.834572   -.4282395
        L4event_b |  -1.704923   .7401593    -2.30   0.022    -3.159222   -.2506226
        L5event_b |  -1.571246    .727352    -2.16   0.031    -3.000382    -.142111
        L6event_b |  -.6937482   .7189663    -0.96   0.335    -2.106407    .7189106
        L7event_b |   1.564636   1.475102     1.06   0.289    -1.333713    4.462985
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -1.602385   1.018886    -1.57   0.116    -3.604341    .3995713
         ew_biodt |   .3644105    .029627    12.30   0.000     .3061981     .422623
        ew_dtmihi |   .0415514   .0538073     0.77   0.440    -.0641716    .1472744
         ew_ledig |   .1965276   .0631614     3.11   0.002     .0724252      .32063
       ew_married |   .3582538   .0637212     5.62   0.000     .2330514    .4834563
        wb_anteil |  -.2757603   .0219907   -12.54   0.000    -.3189687   -.2325519
          wb_ausl |   .0298597   .0158404     1.89   0.060    -.0012642    .0609835
         wb_18t24 |  -.0151428   .0315494    -0.48   0.631    -.0771326     .046847
         wb_25t34 |  -.0525318   .0197912    -2.65   0.008    -.0914185    -.013645
         wb_35t44 |  -.0030325    .023544    -0.13   0.898     -.049293    .0432279
         wb_45t59 |   .0283725   .0223495     1.27   0.205    -.0155409     .072286
          avg_dur |  -.0359945   .0214263    -1.68   0.094     -.078094    .0061049
          hh_kids |   .0065581   .0407393     0.16   0.872    -.0734883    .0866045
mpreis_flats_rent |   .0277091   .0271124     1.02   0.307    -.0255626    .0809807
            _cons |   19.51147   9.201545     2.12   0.034     1.431846     37.5911
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       488         488           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,872
Absorbing 2 HDFE groups                           F(  38,    483) =      13.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9751
                                                  Adj R-squared   =     0.9694
                                                  Within R-sq.    =     0.1973
Number of clusters (sb_new)  =        484         Root MSE        =     1.6236

                                    (Std. err. adjusted for 484 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |   .2871259   .5952808     0.48   0.630    -.8825341    1.456786
        F6event_a |  -.0824268    .604886    -0.14   0.892     -1.27096    1.106106
        F5event_a |   .2229592   .4502853     0.50   0.621    -.6618008    1.107719
        F4event_a |  -.1914561   .2998552    -0.64   0.523    -.7806378    .3977256
        F3event_a |  -.2772888   .2896246    -0.96   0.339    -.8463687    .2917911
        F2event_a |  -.2555493   .2329887    -1.10   0.273    -.7133458    .2022473
        L0event_a |    .293577   .3770293     0.78   0.437    -.4472431    1.034397
        L1event_a |   .0900641   .3837412     0.23   0.815    -.6639443    .8440724
        L2event_a |   .4726482   .3866119     1.22   0.222    -.2870008    1.232297
        L3event_a |   .5348997   .3911261     1.37   0.172    -.2336192    1.303418
        L4event_a |  -.6157297   .8154867    -0.76   0.451    -2.218069      .98661
        L5event_a |   .2569817   1.492653     0.17   0.863    -2.675914    3.189877
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |   -.562455   .4139187    -1.36   0.175    -1.375759    .2508488
        F6event_b |  -.0348081   .3595177    -0.10   0.923    -.7412201    .6716039
        F5event_b |   .1484429   .3411412     0.44   0.664    -.5218613     .818747
        F4event_b |   .1424946   .2634724     0.54   0.589    -.3751991    .6601883
        F3event_b |  -.1454191   .2649285    -0.55   0.583    -.6659738    .3751355
        F2event_b |   .1111502   .2105403     0.53   0.598    -.3025378    .5248382
        L0event_b |    -2.1004   .3375571    -6.22   0.000    -2.763662   -1.437138
        L1event_b |  -2.254947   .3324531    -6.78   0.000     -2.90818   -1.601714
        L2event_b |  -1.725457   .3661024    -4.71   0.000    -2.444807   -1.006107
        L3event_b |  -1.119185   .3592286    -3.12   0.002    -1.825029   -.4133411
        L4event_b |  -1.449391   1.034012    -1.40   0.162     -3.48111    .5823269
        L5event_b |  -1.473784   1.009142    -1.46   0.145    -3.456634    .5090665
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -1.573944   1.016585    -1.55   0.122    -3.571418    .4235312
         ew_biodt |   .3623344   .0297733    12.17   0.000     .3038331    .4208356
        ew_dtmihi |    .040304    .053928     0.75   0.455    -.0656584    .1462664
         ew_ledig |   .1928307    .063573     3.03   0.003     .0679169    .3177444
       ew_married |   .3517958   .0641395     5.48   0.000     .2257689    .4778228
        wb_anteil |  -.2723167   .0220925   -12.33   0.000    -.3157261   -.2289073
          wb_ausl |   .0289039   .0159657     1.81   0.071     -.002467    .0602748
         wb_18t24 |    -.01422   .0316171    -0.45   0.653    -.0763441    .0479042
         wb_25t34 |  -.0527544   .0198908    -2.65   0.008    -.0918375   -.0136713
         wb_35t44 |  -.0030445    .023608    -0.13   0.897    -.0494315    .0433424
         wb_45t59 |   .0287132   .0224115     1.28   0.201    -.0153229    .0727494
          avg_dur |  -.0366799   .0214391    -1.71   0.088    -.0788053    .0054455
          hh_kids |   .0074468   .0408721     0.18   0.856    -.0728623     .087756
mpreis_flats_rent |   .0263746   .0271903     0.97   0.333    -.0270514    .0798005
            _cons |   19.65729   9.203747     2.14   0.033     1.572963    37.74162
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       484         484           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        146        146

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,552
Absorbing 2 HDFE groups                           F(  36,    443) =      14.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9756
                                                  Adj R-squared   =     0.9699
                                                  Within R-sq.    =     0.2065
Number of clusters (sb_new)  =        444         Root MSE        =     1.6110

                                    (Std. err. adjusted for 444 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |   .7075121   .4423378     1.60   0.110    -.1618291    1.576853
        F5event_a |   .2449562   .5303619     0.46   0.644    -.7973818    1.287294
        F4event_a |  -.1296912    .307383    -0.42   0.673    -.7338013    .4744189
        F3event_a |   .0412187   .3300745     0.12   0.901    -.6074878    .6899252
        F2event_a |   -.004555   .2553919    -0.02   0.986    -.5064852    .4973752
        L0event_a |   .1730488   .4382136     0.39   0.693     -.688187    1.034285
        L1event_a |   .1412311   .4047471     0.35   0.727    -.6542319    .9366941
        L2event_a |   .5675846   .3934831     1.44   0.150     -.205741     1.34091
        L3event_a |   .6578892   .3975993     1.65   0.099     -.123526    1.439304
        L4event_a |  -.5393953   .8326842    -0.65   0.517    -2.175897    1.097107
        L5event_a |   .3293767   1.504155     0.22   0.827    -2.626789    3.285543
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |   .4930606   .4934363     1.00   0.318    -.4767061    1.462827
        F5event_b |   .8146143   .4815142     1.69   0.091    -.1317217     1.76095
        F4event_b |   .0622087   .2923528     0.21   0.832    -.5123619    .6367794
        F3event_b |  -.2066245   .3099897    -0.67   0.505    -.8158576    .4026086
        F2event_b |    .229557   .2523357     0.91   0.363    -.2663669    .7254808
        L0event_b |  -2.723179   .3599361    -7.57   0.000    -3.430574   -2.015785
        L1event_b |  -2.383193   .3572297    -6.67   0.000    -3.085268   -1.681117
        L2event_b |  -1.832127   .3905611    -4.69   0.000     -2.59971   -1.064544
        L3event_b |  -1.149252   .3682452    -3.12   0.002    -1.872976   -.4255273
        L4event_b |  -1.549054   1.062055    -1.46   0.145    -3.636346    .5382388
        L5event_b |  -1.570984   1.037714    -1.51   0.131    -3.610438    .4684707
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -1.137277   1.013706    -1.12   0.263    -3.129547    .8549943
         ew_biodt |   .3678777    .029768    12.36   0.000     .3093737    .4263818
        ew_dtmihi |   .0252431   .0564578     0.45   0.655    -.0857152    .1362015
         ew_ledig |   .1990195   .0652928     3.05   0.002     .0706973    .3273417
       ew_married |   .3496251   .0658016     5.31   0.000     .2203031    .4789471
        wb_anteil |  -.2748176   .0217546   -12.63   0.000    -.3175727   -.2320625
          wb_ausl |   .0310275   .0168909     1.84   0.067    -.0021687    .0642236
         wb_18t24 |  -.0151162   .0354224    -0.43   0.670    -.0847332    .0545007
         wb_25t34 |  -.0541238   .0211662    -2.56   0.011    -.0957223   -.0125252
         wb_35t44 |  -.0013222   .0255311    -0.05   0.959    -.0514993    .0488549
         wb_45t59 |    .036007   .0224661     1.60   0.110    -.0081464    .0801604
          avg_dur |   -.031894   .0215481    -1.48   0.140    -.0742432    .0104552
          hh_kids |   -.004852   .0437458    -0.11   0.912    -.0908271    .0811231
mpreis_flats_rent |   .0356191   .0277864     1.28   0.201    -.0189905    .0902287
            _cons |   15.90927   9.449746     1.68   0.093     -2.66263    34.48117
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       444         444           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        106        106

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  32,    437) =      15.34
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9757
                                                  Adj R-squared   =     0.9700
                                                  Within R-sq.    =     0.2103
Number of clusters (sb_new)  =        438         Root MSE        =     1.6125

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |   .7078119   .4488895     1.58   0.116    -.1744387    1.590063
        F5event_a |   .2513892   .5403118     0.47   0.642    -.8105435    1.313322
        F4event_a |  -.1409624   .3196856    -0.44   0.659    -.7692747      .48735
        F3event_a |   .0377278   .3476381     0.11   0.914    -.6455226    .7209782
        F2event_a |   .0186103   .2689694     0.07   0.945    -.5100242    .5472449
        L0event_a |   .1492532   .4629823     0.32   0.747    -.7606956    1.059202
        L1event_a |   .1467557   .4356606     0.34   0.736    -.7094949    1.003006
        L2event_a |   .6188025   .4189949     1.48   0.140    -.2046932    1.442298
        L3event_a |    .643383    .419701     1.53   0.126    -.1815004    1.468266
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |   .4151588   .4939371     0.84   0.401    -.5556289    1.385946
        F5event_b |   .7497095   .4827654     1.55   0.121    -.1991211     1.69854
        F4event_b |   .0131386   .2946572     0.04   0.964    -.5659829    .5922601
        F3event_b |  -.2540322   .3151176    -0.81   0.421    -.8733666    .3653023
        F2event_b |   .2399685   .2628537     0.91   0.362    -.2766462    .7565831
        L0event_b |  -2.883912   .3677716    -7.84   0.000    -3.606733   -2.161091
        L1event_b |  -2.526261   .3678155    -6.87   0.000    -3.249168   -1.803354
        L2event_b |  -1.875234   .4072077    -4.61   0.000    -2.675563   -1.074905
        L3event_b |  -1.094581   .3842426    -2.85   0.005    -1.849774   -.3393876
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -1.115536   1.010767    -1.10   0.270    -3.102105     .871032
         ew_biodt |   .3703078    .029929    12.37   0.000     .3114851    .4291305
        ew_dtmihi |   .0227641   .0565882     0.40   0.688    -.0884548    .1339829
         ew_ledig |   .1901025   .0656108     2.90   0.004     .0611505    .3190544
       ew_married |   .3493693   .0661736     5.28   0.000     .2193113    .4794273
        wb_anteil |  -.2760408   .0218493   -12.63   0.000    -.3189836   -.2330979
          wb_ausl |   .0313871   .0169141     1.86   0.064     -.001856    .0646302
         wb_18t24 |  -.0105741   .0359502    -0.29   0.769    -.0812309    .0600826
         wb_25t34 |  -.0524582   .0213357    -2.46   0.014    -.0943914   -.0105249
         wb_35t44 |   .0011546   .0255937     0.05   0.964    -.0491475    .0514566
         wb_45t59 |   .0382432   .0225846     1.69   0.091    -.0061447    .0826311
          avg_dur |  -.0319161   .0216048    -1.48   0.140    -.0743783     .010546
          hh_kids |  -.0031499   .0438652    -0.07   0.943     -.089363    .0830631
mpreis_flats_rent |   .0369379   .0278846     1.32   0.186    -.0178666    .0917424
            _cons |    15.9204   9.487649     1.68   0.094    -2.726694     34.5675
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  30,    431) =      15.54
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9758
                                                  Adj R-squared   =     0.9700
                                                  Within R-sq.    =     0.2099
Number of clusters (sb_new)  =        432         Root MSE        =     1.6103

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
     turnout_urne | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |   .5211905   .6612172     0.79   0.431    -.7784208    1.820802
        F4event_a |    .024859   .3238628     0.08   0.939     -.611688    .6614059
        F3event_a |    .186852   .3587719     0.52   0.603    -.5183082    .8920122
        F2event_a |   .0853485   .2839568     0.30   0.764     -.472764    .6434609
        L0event_a |   .4122388   .4594521     0.90   0.370    -.4908067    1.315284
        L1event_a |   .4077678   .4287684     0.95   0.342    -.4349693    1.250505
        L2event_a |    .753802   .4239595     1.78   0.076    -.0794834    1.587087
        L3event_a |    .805224   .4181126     1.93   0.055    -.0165692    1.627017
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |   .6023666   .6077144     0.99   0.322    -.5920858    1.796819
        F4event_b |   .0481439   .3128402     0.15   0.878    -.5667383    .6630262
        F3event_b |  -.0940259   .3109917    -0.30   0.763    -.7052748    .5172231
        F2event_b |   .2785513   .2663686     1.05   0.296    -.2449918    .8020944
        L0event_b |  -2.921113   .3893919    -7.50   0.000    -3.686457    -2.15577
        L1event_b |  -2.662371   .3917178    -6.80   0.000    -3.432286   -1.892457
        L2event_b |  -1.878365    .409691    -4.58   0.000    -2.683605   -1.073124
        L3event_b |  -1.062102   .3841721    -2.76   0.006    -1.817186   -.3070179
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |  -1.153993   1.019794    -1.13   0.258    -3.158381     .850395
         ew_biodt |   .3714819   .0302416    12.28   0.000     .3120426    .4309212
        ew_dtmihi |   .0188454   .0578032     0.33   0.745    -.0947659    .1324567
         ew_ledig |   .1921957    .065699     2.93   0.004     .0630654    .3213259
       ew_married |    .346164   .0668376     5.18   0.000     .2147958    .4775321
        wb_anteil |  -.2748316   .0221296   -12.42   0.000     -.318327   -.2313363
          wb_ausl |   .0301201   .0168658     1.79   0.075    -.0030294    .0632695
         wb_18t24 |  -.0123693   .0365381    -0.34   0.735    -.0841843    .0594457
         wb_25t34 |  -.0519388   .0213533    -2.43   0.015    -.0939084   -.0099692
         wb_35t44 |   .0043876   .0257089     0.17   0.865    -.0461429     .054918
         wb_45t59 |   .0358437   .0229444     1.56   0.119    -.0092532    .0809406
          avg_dur |  -.0292881   .0220401    -1.33   0.185    -.0726077    .0140314
          hh_kids |   .0021702   .0459521     0.05   0.962    -.0881479    .0924883
mpreis_flats_rent |   .0363794   .0281238     1.29   0.197    -.0188974    .0916562
            _cons |   15.97834   9.643718     1.66   0.098    -2.976223    34.93291
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  42,    487) =      13.93
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9633
                                                  Adj R-squared   =     0.9549
                                                  Within R-sq.    =     0.2418
Number of clusters (sb_new)  =        488         Root MSE        =     1.6355

                                    (Std. err. adjusted for 488 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.7261702   .5759354    -1.26   0.208    -1.857795    .4054548
        F6event_a |  -.0288883   .4965592    -0.06   0.954    -1.004551    .9467745
        F5event_a |  -1.145788   .4744032    -2.42   0.016    -2.077918   -.2136587
        F4event_a |   -.238669   .2780422    -0.86   0.391    -.7849794    .3076414
        F3event_a |    .340101    .249409     1.36   0.173    -.1499496    .8301516
        F2event_a |  -.0317321   .2195667    -0.14   0.885     -.463147    .3996828
        L0event_a |  -.0625793   .3610025    -0.17   0.862    -.7718939    .6467354
        L1event_a |   .2388751   .3656723     0.65   0.514     -.479615    .9573651
        L2event_a |   .4009391   .4172321     0.96   0.337    -.4188581    1.220736
        L3event_a |  -.4551611   .3723428    -1.22   0.222    -1.186758    .2764357
        L4event_a |  -.1306071   1.307297    -0.10   0.920    -2.699246    2.438032
        L5event_a |   1.508117   .9382007     1.61   0.109    -.3353043    3.351538
        L6event_a |  -2.501584   .7311122    -3.42   0.001    -3.938108    -1.06506
        L7event_a |  -1.309427    .718194    -1.82   0.069    -2.720568    .1017146
        F7event_b |   .5460889   .3366882     1.62   0.105    -.1154519     1.20763
        F6event_b |   .5527328   .2600551     2.13   0.034     .0417643    1.063701
        F5event_b |  -.3578404   .3968264    -0.90   0.368    -1.137543    .4218628
        F4event_b |  -.1900173    .263725    -0.72   0.472    -.7081966     .328162
        F3event_b |   .1518828   .2218497     0.68   0.494    -.2840179    .5877834
        F2event_b |  -.1709144   .1792747    -0.95   0.341    -.5231618    .1813331
        L0event_b |   1.060333   .3274571     3.24   0.001     .4169299    1.703736
        L1event_b |   1.739747   .3318292     5.24   0.000     1.087753     2.39174
        L2event_b |   1.703535   .3827121     4.45   0.000     .9515646    2.455506
        L3event_b |   1.181864   .3594777     3.29   0.001     .4755454    1.888183
        L4event_b |   2.499703   .5179997     4.83   0.000     1.481913    3.517493
        L5event_b |   2.561442   .5839539     4.39   0.000     1.414062    3.708822
        L6event_b |    1.68862   .7488216     2.26   0.025     .2172997     3.15994
        L7event_b |   .3571357   1.270638     0.28   0.779    -2.139474    2.853745
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.447912   1.341862     1.82   0.069    -.1886409    5.084465
         ew_biodt |   .3936837   .0296789    13.26   0.000     .3353692    .4519982
        ew_dtmihi |  -.1977065   .0624963    -3.16   0.002    -.3205022   -.0749107
         ew_ledig |   .2835538   .0751524     3.77   0.000     .1358909    .4312168
       ew_married |   .3328909   .0758377     4.39   0.000     .1838815    .4819003
        wb_anteil |  -.2551912   .0229521   -11.12   0.000    -.3002885   -.2100938
          wb_ausl |  -.0704014   .0154313    -4.56   0.000    -.1007217   -.0400812
         wb_18t24 |  -.0285341   .0297604    -0.96   0.338    -.0870088    .0299405
         wb_25t34 |   .0364492   .0200755     1.82   0.070     -.002996    .0758945
         wb_35t44 |  -.0205179   .0255645    -0.80   0.423    -.0707482    .0297124
         wb_45t59 |  -.0447367   .0213156    -2.10   0.036    -.0866186   -.0028548
          avg_dur |   .0364029   .0241443     1.51   0.132     -.011037    .0838429
          hh_kids |  -.1054304    .045906    -2.30   0.022    -.1956288    -.015232
mpreis_flats_rent |  -.0154267   .0270349    -0.57   0.569    -.0685461    .0376926
            _cons |  -18.04105   11.52672    -1.57   0.118    -40.68929    4.607199
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       488         488           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,872
Absorbing 2 HDFE groups                           F(  38,    483) =      14.01
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9633
                                                  Adj R-squared   =     0.9550
                                                  Within R-sq.    =     0.2415
Number of clusters (sb_new)  =        484         Root MSE        =     1.6353

                                    (Std. err. adjusted for 484 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.7143626   .5772947    -1.24   0.217    -1.848682    .4199567
        F6event_a |  -.0171764   .4986255    -0.03   0.973    -.9969195    .9625667
        F5event_a |  -1.149733   .4755889    -2.42   0.016    -2.084212   -.2152543
        F4event_a |  -.2358922   .2780248    -0.85   0.397    -.7821797    .3103954
        F3event_a |   .3434825   .2483139     1.38   0.167    -.1444264    .8313913
        F2event_a |  -.0390336   .2197336    -0.18   0.859    -.4707854    .3927183
        L0event_a |  -.0904593   .3616701    -0.25   0.803    -.8011004    .6201818
        L1event_a |   .1936639   .3671643     0.53   0.598    -.5277727    .9151004
        L2event_a |   .4256987   .4226665     1.01   0.314    -.4047934    1.256191
        L3event_a |  -.4819726   .3772707    -1.28   0.202    -1.223267     .259322
        L4event_a |   2.158199   .8506696     2.54   0.011     .4867285    3.829669
        L5event_a |   2.514683   1.067871     2.35   0.019     .4164364     4.61293
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |   .5673243   .3342256     1.70   0.090    -.0893915     1.22404
        F6event_b |   .5723061    .258434     2.21   0.027     .0645123      1.0801
        F5event_b |  -.3526442   .3975058    -0.89   0.375    -1.133699    .4284101
        F4event_b |  -.1674059   .2632299    -0.64   0.525    -.6846231    .3498113
        F3event_b |   .1732539   .2209478     0.78   0.433    -.2608838    .6073915
        F2event_b |  -.1761922   .1796869    -0.98   0.327    -.5292567    .1768723
        L0event_b |   1.026339   .3294498     3.12   0.002     .3790074    1.673671
        L1event_b |   1.692296    .333241     5.08   0.000     1.037515    2.347077
        L2event_b |   1.765904   .3853156     4.58   0.000     1.008803    2.523006
        L3event_b |   1.177234   .3610573     3.26   0.001     .4677967    1.886671
        L4event_b |    3.33439   .6271578     5.32   0.000     2.102095    4.566684
        L5event_b |   2.575739   .7757595     3.32   0.001     1.051459    4.100019
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.432567   1.349216     1.80   0.072    -.2184907    5.083624
         ew_biodt |   .3962819   .0297446    13.32   0.000     .3378372    .4547266
        ew_dtmihi |  -.2019029    .062646    -3.22   0.001    -.3249953   -.0788105
         ew_ledig |   .2758848   .0755499     3.65   0.000     .1274376    .4243319
       ew_married |   .3261475   .0763422     4.27   0.000     .1761437    .4761513
        wb_anteil |  -.2571498   .0231461   -11.11   0.000    -.3026293   -.2116703
          wb_ausl |  -.0688765   .0154332    -4.46   0.000    -.0992009    -.038552
         wb_18t24 |  -.0296894   .0296601    -1.00   0.317     -.087968    .0285893
         wb_25t34 |   .0374029   .0201843     1.85   0.064     -.002257    .0770627
         wb_35t44 |  -.0204884   .0256209    -0.80   0.424    -.0708306    .0298539
         wb_45t59 |  -.0447755   .0213582    -2.10   0.037    -.0867419   -.0028091
          avg_dur |   .0370562   .0241782     1.53   0.126    -.0104513    .0845637
          hh_kids |  -.1003635   .0458857    -2.19   0.029    -.1905237   -.0102033
mpreis_flats_rent |  -.0132499   .0270842    -0.49   0.625    -.0664674    .0399677
            _cons |   -17.4267   11.54499    -1.51   0.132     -40.1113    5.257908
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       484         484           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        146        146

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,552
Absorbing 2 HDFE groups                           F(  36,    443) =      14.69
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9638
                                                  Adj R-squared   =     0.9552
                                                  Within R-sq.    =     0.2575
Number of clusters (sb_new)  =        444         Root MSE        =     1.6275

                                    (Std. err. adjusted for 444 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |  -1.001001   .6818327    -1.47   0.143     -2.34103    .3390274
        F5event_a |  -.2855947   .4431375    -0.64   0.520    -1.156508    .5853181
        F4event_a |   .0344511   .2549508     0.14   0.893    -.4666122    .5355143
        F3event_a |   .0788577   .3032267     0.26   0.795    -.5170838    .6747993
        F2event_a |  -.0767003   .2441454    -0.31   0.754    -.5565274    .4031268
        L0event_a |   .4833791   .4164101     1.16   0.246    -.3350055    1.301764
        L1event_a |    .336749   .3873616     0.87   0.385    -.4245457    1.098044
        L2event_a |   .5793432   .4448828     1.30   0.194    -.2949998    1.453686
        L3event_a |  -.4286877   .3881952    -1.10   0.270    -1.191621    .3342453
        L4event_a |    2.35309    .859838     2.74   0.006      .663222    4.042958
        L5event_a |   2.668815    1.07531     2.48   0.013     .5554728    4.782157
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |   .0634061   .5101095     0.12   0.901    -.9391291    1.065941
        F5event_b |  -.2756923   .3468935    -0.79   0.427    -.9574537    .4060691
        F4event_b |   -.088153   .2434171    -0.36   0.717    -.5665488    .3902427
        F3event_b |    .110847   .2939297     0.38   0.706    -.4668229    .6885169
        F2event_b |   -.168363   .2095806    -0.80   0.422    -.5802588    .2435328
        L0event_b |   1.854686   .3333694     5.56   0.000     1.199504    2.509868
        L1event_b |   1.896292    .352631     5.38   0.000     1.203255     2.58933
        L2event_b |   1.950341   .4062326     4.80   0.000     1.151959    2.748724
        L3event_b |   1.217985   .3739294     3.26   0.001      .483089    1.952881
        L4event_b |   3.584322   .6414116     5.59   0.000     2.323735     4.84491
        L5event_b |   2.780445   .7968182     3.49   0.001     1.214432    4.346459
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.208163   1.355505     1.63   0.104    -.4558553    4.872182
         ew_biodt |   .4111103   .0306532    13.41   0.000     .3508665    .4713541
        ew_dtmihi |  -.1819769   .0655842    -2.77   0.006    -.3108717   -.0530822
         ew_ledig |   .2934691   .0798061     3.68   0.000     .1366235    .4503148
       ew_married |   .3406919   .0795582     4.28   0.000     .1843336    .4970502
        wb_anteil |  -.2614587   .0238646   -10.96   0.000    -.3083606   -.2145569
          wb_ausl |  -.0700976   .0162195    -4.32   0.000    -.1019743   -.0382208
         wb_18t24 |  -.0326671   .0327883    -1.00   0.320     -.097107    .0317727
         wb_25t34 |   .0433424   .0216753     2.00   0.046     .0007432    .0859416
         wb_35t44 |  -.0222116   .0276244    -0.80   0.422    -.0765027    .0320795
         wb_45t59 |  -.0549032   .0219899    -2.50   0.013    -.0981207   -.0116856
          avg_dur |   .0325018   .0245969     1.32   0.187    -.0158394    .0808429
          hh_kids |  -.0909463   .0489315    -1.86   0.064    -.1871131    .0052205
mpreis_flats_rent |  -.0123969   .0277893    -0.45   0.656    -.0670121    .0422183
            _cons |  -18.05102   11.98177    -1.51   0.133     -41.5992    5.497159
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       444         444           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        106        106

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  32,    437) =      15.51
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9634
                                                  Adj R-squared   =     0.9547
                                                  Within R-sq.    =     0.2556
Number of clusters (sb_new)  =        438         Root MSE        =     1.6310

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |  -1.040142   .6787715    -1.53   0.126    -2.374204    .2939207
        F5event_a |  -.3257395   .4437194    -0.73   0.463    -1.197829    .5463498
        F4event_a |  -.0133296   .2572387    -0.05   0.959    -.5189084    .4922491
        F3event_a |   .0185051   .3061943     0.06   0.952    -.5832915    .6203017
        F2event_a |  -.1355253   .2550186    -0.53   0.595    -.6367408    .3656902
        L0event_a |   .4278163   .4395924     0.97   0.331    -.4361619    1.291794
        L1event_a |   .3485125   .4146808     0.84   0.401    -.4665042    1.163529
        L2event_a |   .5548039   .4736446     1.17   0.242    -.3761006    1.485708
        L3event_a |  -.5501343   .3958532    -1.39   0.165    -1.328147    .2278783
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |   .0998223   .5092122     0.20   0.845    -.9009871    1.100632
        F5event_b |  -.2489208   .3465731    -0.72   0.473    -.9300781    .4322364
        F4event_b |  -.0756177   .2435024    -0.31   0.756     -.554199    .4029637
        F3event_b |   .1220172   .2952059     0.41   0.680    -.4581827    .7022171
        F2event_b |  -.1705213   .2168099    -0.79   0.432    -.5966411    .2555984
        L0event_b |   1.962788   .3371044     5.82   0.000     1.300241    2.625336
        L1event_b |   1.970282    .357036     5.52   0.000     1.268561    2.672003
        L2event_b |   1.939571      .4211     4.61   0.000     1.111938    2.767204
        L3event_b |   1.111894   .3869319     2.87   0.004     .3514157    1.872373
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.264265   1.365739     1.66   0.098    -.4199681    4.948499
         ew_biodt |    .411194   .0307643    13.37   0.000     .3507296    .4716583
        ew_dtmihi |    -.18061   .0658244    -2.74   0.006    -.3099818   -.0512382
         ew_ledig |   .2992406   .0806579     3.71   0.000     .1407151    .4577662
       ew_married |   .3393349   .0803281     4.22   0.000     .1814575    .4972123
        wb_anteil |  -.2600512   .0239719   -10.85   0.000    -.3071657   -.2129367
          wb_ausl |  -.0699845   .0162626    -4.30   0.000     -.101947   -.0380219
         wb_18t24 |  -.0338157   .0331348    -1.02   0.308    -.0989391    .0313078
         wb_25t34 |    .040773   .0218005     1.87   0.062     -.002074    .0836199
         wb_35t44 |  -.0236152   .0276542    -0.85   0.394     -.077967    .0307365
         wb_45t59 |  -.0541938   .0221359    -2.45   0.015    -.0976997   -.0106878
          avg_dur |   .0320587   .0246312     1.30   0.194    -.0163516    .0804691
          hh_kids |  -.0923745   .0491282    -1.88   0.061    -.1889314    .0041824
mpreis_flats_rent |  -.0117065   .0278469    -0.42   0.674     -.066437    .0430241
            _cons |  -18.75916   12.08167    -1.55   0.121    -42.50457    4.986242
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  30,    431) =      15.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9635
                                                  Adj R-squared   =     0.9549
                                                  Within R-sq.    =     0.2553
Number of clusters (sb_new)  =        432         Root MSE        =     1.6279

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_pos_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |  -.3947259   .4164795    -0.95   0.344    -1.213309    .4238575
        F4event_a |  -.1178509   .2665174    -0.44   0.659    -.6416864    .4059846
        F3event_a |  -.0939996   .3201083    -0.29   0.769    -.7231672     .535168
        F2event_a |  -.1950082   .2689378    -0.73   0.469    -.7236009    .3335845
        L0event_a |   .2334228   .4306433     0.54   0.588    -.6129993    1.079845
        L1event_a |   .1515734   .4247009     0.36   0.721    -.6831692    .9863159
        L2event_a |   .4135249   .4810879     0.86   0.391    -.5320454    1.359095
        L3event_a |  -.6598514   .4021453    -1.64   0.102    -1.450261    .1305584
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |  -.1887706   .3362847    -0.56   0.575    -.8497326    .4721914
        F4event_b |  -.2313418     .22723    -1.02   0.309    -.6779587    .2152751
        F3event_b |  -.0123835   .2987876    -0.04   0.967    -.5996454    .5748785
        F2event_b |  -.2668218    .218637    -1.22   0.223    -.6965491    .1629055
        L0event_b |   2.056412   .3618781     5.68   0.000     1.345147    2.767678
        L1event_b |   1.996455   .3848052     5.19   0.000     1.240127    2.752783
        L2event_b |   1.872455   .4270366     4.38   0.000     1.033122    2.711788
        L3event_b |   1.058384   .3934224     2.69   0.007     .2851185    1.831649
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   2.246547   1.365485     1.65   0.101    -.4372906    4.930384
         ew_biodt |   .4056287   .0308576    13.15   0.000     .3449786    .4662788
        ew_dtmihi |  -.1780644   .0671356    -2.65   0.008    -.3100183   -.0461104
         ew_ledig |   .3024381   .0815232     3.71   0.000     .1422056    .4626705
       ew_married |   .3387201   .0816048     4.15   0.000     .1783273     .499113
        wb_anteil |  -.2557654   .0241522   -10.59   0.000    -.3032361   -.2082947
          wb_ausl |  -.0707518   .0163127    -4.34   0.000    -.1028142   -.0386895
         wb_18t24 |  -.0345468   .0336244    -1.03   0.305    -.1006349    .0315414
         wb_25t34 |   .0387892   .0219566     1.77   0.078    -.0043662    .0819446
         wb_35t44 |  -.0260548   .0277822    -0.94   0.349    -.0806602    .0285507
         wb_45t59 |  -.0525945   .0223954    -2.35   0.019    -.0966123   -.0085768
          avg_dur |   .0327466   .0252149     1.30   0.195    -.0168129    .0823061
          hh_kids |  -.0942543   .0517602    -1.82   0.069    -.1959882    .0074795
mpreis_flats_rent |  -.0129017   .0282284    -0.46   0.648    -.0683842    .0425807
            _cons |  -18.62567   12.17138    -1.53   0.127    -42.54831    5.296966
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,904
Absorbing 2 HDFE groups                           F(  42,    487) =      33.49
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4562
Number of clusters (sb_new)  =        488         Root MSE        =     1.6136

                                    (Std. err. adjusted for 488 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.4230042   .5206737    -0.81   0.417    -1.446048      .60004
        F6event_a |   -.096458   .4366312    -0.22   0.825    -.9543716    .7614555
        F5event_a |  -.9137145   .5289644    -1.73   0.085    -1.953049    .1256197
        F4event_a |  -.4151751   .3194088    -1.30   0.194    -1.042765    .2124144
        F3event_a |   .0764664   .2558594     0.30   0.765    -.4262582    .5791911
        F2event_a |  -.2907609   .2649011    -1.10   0.273    -.8112511    .2297293
        L0event_a |   .2456579   .2874436     0.85   0.393    -.3191247    .8104406
        L1event_a |   .2984428   .3388044     0.88   0.379     -.367256    .9641417
        L2event_a |    .857475   .3170351     2.70   0.007     .2345496      1.4804
        L3event_a |   .0995008   .3162348     0.31   0.753    -.5218522    .7208537
        L4event_a |  -.1396961   1.214366    -0.12   0.908    -2.525739    2.246347
        L5event_a |   1.933684   1.273463     1.52   0.130    -.5684758    4.435844
        L6event_a |   .3873514   1.262122     0.31   0.759    -2.092526    2.867229
        L7event_a |  -1.187848   .8559917    -1.39   0.166     -2.86974    .4940451
        F7event_b |   .0004664   .4060461     0.00   0.999    -.7973521    .7982849
        F6event_b |   .5358016   .3596196     1.49   0.137    -.1707958    1.242399
        F5event_b |  -.2020917   .3974147    -0.51   0.611    -.9829509    .5787675
        F4event_b |  -.0320646   .2552462    -0.13   0.900    -.5335844    .4694553
        F3event_b |   .0271708   .2538822     0.11   0.915    -.4716688    .5260104
        F2event_b |  -.0651771   .2108368    -0.31   0.757    -.4794392     .349085
        L0event_b |  -1.036961   .2578481    -4.02   0.000    -1.543593   -.5303285
        L1event_b |  -.5120573   .3076564    -1.66   0.097    -1.116555    .0924404
        L2event_b |   .0185489   .3037367     0.06   0.951    -.5782472     .615345
        L3event_b |   .0504586   .3446479     0.15   0.884    -.6267217     .727639
        L4event_b |   .7947804   .6687999     1.19   0.235    -.5193091     2.10887
        L5event_b |    .990195    .498868     1.98   0.048     .0099955    1.970394
        L6event_b |   .9948686   .5391774     1.85   0.066    -.0645326     2.05427
        L7event_b |   1.921772   .5412334     3.55   0.000     .8583312    2.985213
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   .8455272   1.007871     0.84   0.402    -1.134785    2.825839
         ew_biodt |   .7580942   .0324829    23.34   0.000     .6942703    .8219181
        ew_dtmihi |   -.156155   .0530957    -2.94   0.003    -.2604798   -.0518301
         ew_ledig |   .4800817   .0661559     7.26   0.000     .3500955    .6100679
       ew_married |   .6911449   .0667387    10.36   0.000     .5600136    .8222762
        wb_anteil |  -.5309515   .0243508   -21.80   0.000     -.578797    -.483106
          wb_ausl |  -.0405418   .0198839    -2.04   0.042    -.0796106    -.001473
         wb_18t24 |  -.0436769   .0273079    -1.60   0.110    -.0973328     .009979
         wb_25t34 |  -.0160825   .0180814    -0.89   0.374    -.0516097    .0194447
         wb_35t44 |  -.0235504   .0223137    -1.06   0.292    -.0673934    .0202926
         wb_45t59 |  -.0163641   .0205079    -0.80   0.425     -.056659    .0239308
          avg_dur |   .0004084   .0239927     0.02   0.986    -.0467335    .0475503
          hh_kids |  -.0988724   .0383439    -2.58   0.010    -.1742123   -.0235325
mpreis_flats_rent |   .0122823   .0261869     0.47   0.639     -.039171    .0637356
            _cons |   1.470414   9.882275     0.15   0.882    -17.94675    20.88757
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       488         488           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        150        150

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,872
Absorbing 2 HDFE groups                           F(  38,    483) =      35.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9904
                                                  Adj R-squared   =     0.9882
                                                  Within R-sq.    =     0.4562
Number of clusters (sb_new)  =        484         Root MSE        =     1.6146

                                    (Std. err. adjusted for 484 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |  -.4272378   .5225257    -0.82   0.414    -1.453942    .5994665
        F6event_a |  -.0996039   .4391331    -0.23   0.821    -.9624512    .7632434
        F5event_a |  -.9267728   .5304377    -1.75   0.081    -1.969023    .1154777
        F4event_a |  -.4273477   .3201334    -1.33   0.183    -1.056374    .2016785
        F3event_a |    .066193   .2558936     0.26   0.796    -.4366093    .5689953
        F2event_a |  -.2945821   .2661683    -1.11   0.269    -.8175729    .2284088
        L0event_a |   .2031174   .2884968     0.70   0.482    -.3637464    .7699812
        L1event_a |   .2837276   .3443702     0.82   0.410    -.3929212    .9603764
        L2event_a |   .8983473   .3208386     2.80   0.005     .2679355    1.528759
        L3event_a |    .052927   .3239936     0.16   0.870    -.5836839     .689538
        L4event_a |    1.54247   .6443313     2.39   0.017     .2764318    2.808509
        L5event_a |   2.771666   1.734932     1.60   0.111    -.6372798    6.180613
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |   .0048696    .404129     0.01   0.990    -.7891984    .7989377
        F6event_b |   .5374973   .3586062     1.50   0.135    -.1671237    1.242118
        F5event_b |  -.2042013    .397029    -0.51   0.607    -.9843186    .5759161
        F4event_b |  -.0249107   .2558399    -0.10   0.922    -.5276073    .4777859
        F3event_b |   .0278345   .2545816     0.11   0.913    -.4723898    .5280587
        F2event_b |  -.0650415   .2123939    -0.31   0.760    -.4823718    .3522887
        L0event_b |   -1.07406   .2597303    -4.14   0.000    -1.584401   -.5637191
        L1event_b |  -.5626504   .3119276    -1.80   0.072    -1.175553    .0502522
        L2event_b |   .0404475   .3072377     0.13   0.895      -.56324     .644135
        L3event_b |   .0580493   .3520686     0.16   0.869    -.6337259    .7498245
        L4event_b |   1.884998   .7035878     2.68   0.008     .5025266    3.267469
        L5event_b |   1.101956    .583588     1.89   0.060    -.0447289    2.248641
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |    .858623   1.011556     0.85   0.396     -1.12897    2.846216
         ew_biodt |   .7586163   .0325489    23.31   0.000     .6946614    .8225711
        ew_dtmihi |  -.1615988   .0531454    -3.04   0.002    -.2660236    -.057174
         ew_ledig |   .4687156   .0660563     7.10   0.000     .3389223    .5985089
       ew_married |   .6779435   .0665945    10.18   0.000     .5470928    .8087942
        wb_anteil |  -.5294666   .0245109   -21.60   0.000    -.5776277   -.4813054
          wb_ausl |  -.0399726   .0199217    -2.01   0.045    -.0791165   -.0008287
         wb_18t24 |  -.0439093   .0273061    -1.61   0.108    -.0975628    .0097442
         wb_25t34 |  -.0153515   .0182112    -0.84   0.400    -.0511346    .0204315
         wb_35t44 |  -.0235329   .0223335    -1.05   0.293    -.0674156    .0203499
         wb_45t59 |  -.0160623   .0205466    -0.78   0.435    -.0564341    .0243096
          avg_dur |   .0003763   .0240857     0.02   0.988    -.0469493    .0477019
          hh_kids |  -.0929168   .0381731    -2.43   0.015    -.1679226   -.0179109
mpreis_flats_rent |   .0131247   .0262962     0.50   0.618    -.0385443    .0647938
            _cons |   2.230583   9.889149     0.23   0.822    -17.20048    21.66165
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       484         484           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        146        146

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,552
Absorbing 2 HDFE groups                           F(  36,    443) =      35.29
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9881
                                                  Within R-sq.    =     0.4638
Number of clusters (sb_new)  =        444         Root MSE        =     1.6261

                                    (Std. err. adjusted for 444 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |  -.2934899   .4828298    -0.61   0.544    -1.242411    .6554317
        F5event_a |  -.0406367   .5495371    -0.07   0.941     -1.12066    1.039387
        F4event_a |  -.0952399   .2909671    -0.33   0.744    -.6670873    .4766076
        F3event_a |   .1200756   .3217205     0.37   0.709    -.5122124    .7523635
        F2event_a |  -.0812546   .3033194    -0.27   0.789    -.6773783    .5148692
        L0event_a |   .6564275   .3024641     2.17   0.031     .0619846     1.25087
        L1event_a |   .4779796   .3534897     1.35   0.177    -.2167455    1.172705
        L2event_a |   1.146928   .3211697     3.57   0.000     .5157227    1.778134
        L3event_a |   .2292014   .3239282     0.71   0.480    -.4074255    .8658284
        L4event_a |   1.813696   .6552069     2.77   0.006     .5259963    3.101396
        L5event_a |   2.998193   1.755908     1.71   0.088    -.4527514    6.449137
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |   .5564668   .8732329     0.64   0.524    -1.159727    2.272661
        F5event_b |   .5389223   .4543475     1.19   0.236     -.354022    1.431867
        F4event_b |  -.0259435    .310472    -0.08   0.933    -.6361244    .5842375
        F3event_b |  -.0957782   .2983926    -0.32   0.748    -.6822192    .4906628
        F2event_b |   .0611943   .2611536     0.23   0.815    -.4520595    .5744482
        L0event_b |  -.8684923   .3158179    -2.75   0.006     -1.48918   -.2478048
        L1event_b |     -.4869   .3407958    -1.43   0.154    -1.156677    .1828773
        L2event_b |   .1182137   .3326561     0.36   0.722    -.5355663    .7719938
        L3event_b |   .0687336   .3751756     0.18   0.855    -.6686116    .8060788
        L4event_b |   2.035268   .7150813     2.85   0.005     .6298949    3.440641
        L5event_b |   1.209462    .596926     2.03   0.043     .0363031     2.38262
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.070887   1.067666     1.00   0.316    -1.027432    3.169205
         ew_biodt |    .778988   .0341765    22.79   0.000     .7118197    .8461563
        ew_dtmihi |  -.1567337   .0554017    -2.83   0.005    -.2656164   -.0478509
         ew_ledig |   .4924888   .0697378     7.06   0.000     .3554308    .6295468
       ew_married |   .6903171   .0693773     9.95   0.000     .5539675    .8266667
        wb_anteil |  -.5362763   .0258102   -20.78   0.000     -.587002   -.4855507
          wb_ausl |  -.0390701   .0209208    -1.87   0.062    -.0801866    .0020463
         wb_18t24 |  -.0477833   .0291714    -1.64   0.102    -.1051149    .0095482
         wb_25t34 |  -.0107813   .0195682    -0.55   0.582    -.0492394    .0276767
         wb_35t44 |  -.0235338   .0241958    -0.97   0.331    -.0710867    .0240191
         wb_45t59 |  -.0188961   .0216451    -0.87   0.383    -.0614361    .0236438
          avg_dur |   .0006078   .0249684     0.02   0.981    -.0484634     .049679
          hh_kids |  -.0957985   .0401907    -2.38   0.018    -.1747867   -.0168103
mpreis_flats_rent |   .0232222   .0272842     0.85   0.395    -.0304004    .0768448
            _cons |  -2.141763   10.43423    -0.21   0.837    -22.64851    18.36499
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       444         444           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        106        106

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,504
Absorbing 2 HDFE groups                           F(  32,    437) =      39.18
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9880
                                                  Within R-sq.    =     0.4639
Number of clusters (sb_new)  =        438         Root MSE        =     1.6311

                                    (Std. err. adjusted for 438 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |  -.3323305   .4922235    -0.68   0.500     -1.29975    .6350892
        F5event_a |  -.0743482   .5543842    -0.13   0.893    -1.163939    1.015243
        F4event_a |  -.1542914   .3045284    -0.51   0.613    -.7528136    .4442309
        F3event_a |   .0562322    .341513     0.16   0.869    -.6149799    .7274443
        F2event_a |  -.1169141   .3285407    -0.36   0.722    -.7626303    .5288022
        L0event_a |   .5770692   .3295737     1.75   0.081    -.0706772    1.224816
        L1event_a |   .4952682   .3902729     1.27   0.205    -.2717771    1.262313
        L2event_a |   1.173607   .3416588     3.44   0.001     .5021085    1.845106
        L3event_a |   .0932488   .3394867     0.27   0.784    -.5739809    .7604785
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |   .5149811    .872452     0.59   0.555    -1.199742    2.229705
        F5event_b |   .5007889   .4575722     1.09   0.274    -.3985268    1.400105
        F4event_b |  -.0624783   .3155074    -0.20   0.843    -.6825789    .5576223
        F3event_b |  -.1320157   .3038481    -0.43   0.664     -.729201    .4651696
        F2event_b |   .0694473   .2710222     0.26   0.798    -.4632218    .6021164
        L0event_b |  -.9211231   .3315045    -2.78   0.006    -1.572665   -.2695817
        L1event_b |  -.5559783   .3486739    -1.59   0.112    -1.241265    .1293079
        L2event_b |   .0643366   .3473774     0.19   0.853    -.6184016    .7470747
        L3event_b |    .017314   .3950885     0.04   0.965    -.7591959    .7938239
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.148729   1.077671     1.07   0.287    -.9693344    3.266792
         ew_biodt |   .7815018   .0343596    22.74   0.000     .7139713    .8490323
        ew_dtmihi |  -.1578458   .0555301    -2.84   0.005    -.2669851   -.0487064
         ew_ledig |   .4893433   .0702909     6.96   0.000      .351193    .6274936
       ew_married |   .6887043   .0700416     9.83   0.000      .551044    .8263646
        wb_anteil |   -.536092   .0259501   -20.66   0.000    -.5870946   -.4850894
          wb_ausl |  -.0385974   .0209752    -1.84   0.066    -.0798222    .0026274
         wb_18t24 |  -.0443897   .0295158    -1.50   0.133    -.1024003    .0136209
         wb_25t34 |  -.0116852   .0197426    -0.59   0.554    -.0504875    .0271171
         wb_35t44 |  -.0224606   .0242407    -0.93   0.355    -.0701035    .0251822
         wb_45t59 |  -.0159506   .0217323    -0.73   0.463    -.0586635    .0267623
          avg_dur |   .0001426    .025014     0.01   0.995    -.0490202    .0493054
          hh_kids |  -.0955245   .0403313    -2.37   0.018    -.1747919   -.0162572
mpreis_flats_rent |   .0252314   .0275314     0.92   0.360    -.0288791    .0793419
            _cons |  -2.838779   10.53685    -0.27   0.788    -23.54797    17.87042
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       438         438           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |        100        100

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338
(MWFE estimator converged in 5 iterations)
note: F7event_a omitted because of collinearity
note: F6event_a omitted because of collinearity
note: L4event_a omitted because of collinearity
note: L5event_a omitted because of collinearity
note: L6event_a omitted because of collinearity
note: L7event_a omitted because of collinearity
note: F7event_b omitted because of collinearity
note: F6event_b omitted because of collinearity
note: L4event_b omitted because of collinearity
note: L5event_b omitted because of collinearity
note: L6event_b omitted because of collinearity
note: L7event_b omitted because of collinearity
note: F1event_a omitted because of collinearity
note: F1event_b omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,456
Absorbing 2 HDFE groups                           F(  30,    431) =      39.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9903
                                                  Adj R-squared   =     0.9880
                                                  Within R-sq.    =     0.4614
Number of clusters (sb_new)  =        432         Root MSE        =     1.6328

                                    (Std. err. adjusted for 432 clusters in sb_new)
-----------------------------------------------------------------------------------
                  |               Robust
  turnout_tot_req | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
        F7event_a |          0  (omitted)
        F6event_a |          0  (omitted)
        F5event_a |   .1264662   .7397831     0.17   0.864    -1.327565    1.580498
        F4event_a |  -.0929914   .3206991    -0.29   0.772    -.7233201    .5373372
        F3event_a |   .0928516    .363129     0.26   0.798    -.6208724    .8065756
        F2event_a |  -.1096588   .3479854    -0.32   0.753    -.7936183    .5743006
        L0event_a |   .6456614   .3448848     1.87   0.062    -.0322039    1.323527
        L1event_a |    .559341   .4083973     1.37   0.172    -.2433572    1.362039
        L2event_a |   1.167327   .3534452     3.30   0.001     .4726368    1.862018
        L3event_a |   .1453728   .3476069     0.42   0.676    -.5378429    .8285884
        L4event_a |          0  (omitted)
        L5event_a |          0  (omitted)
        L6event_a |          0  (omitted)
        L7event_a |          0  (omitted)
        F7event_b |          0  (omitted)
        F6event_b |          0  (omitted)
        F5event_b |   .4135972    .511283     0.81   0.419     -.591321    1.418515
        F4event_b |  -.1831969   .3134659    -0.58   0.559    -.7993088     .432915
        F3event_b |  -.1064101   .3263177    -0.33   0.745    -.7477821    .5349618
        F2event_b |   .0117296   .2891517     0.04   0.968    -.5565933    .5800525
        L0event_b |  -.8647002    .355324    -2.43   0.015    -1.563084   -.1663167
        L1event_b |  -.6659159   .3748089    -1.78   0.076    -1.402597    .0707647
        L2event_b |  -.0059096   .3566388    -0.02   0.987    -.7068772    .6950579
        L3event_b |  -.0037177   .4052671    -0.01   0.993    -.8002634     .792828
        L4event_b |          0  (omitted)
        L5event_b |          0  (omitted)
        L6event_b |          0  (omitted)
        L7event_b |          0  (omitted)
        F1event_a |          0  (omitted)
        F1event_b |          0  (omitted)
        ln_ew_ges |   1.092553   1.074355     1.02   0.310    -1.019074    3.204181
         ew_biodt |   .7771106   .0347557    22.36   0.000     .7087989    .8454223
        ew_dtmihi |  -.1592188   .0563688    -2.82   0.005    -.2700107   -.0484269
         ew_ledig |   .4946339   .0708485     6.98   0.000     .3553824    .6338854
       ew_married |   .6848842   .0707442     9.68   0.000     .5458377    .8239308
        wb_anteil |  -.5305971   .0260897   -20.34   0.000    -.5818758   -.4793183
          wb_ausl |  -.0406318   .0213168    -1.91   0.057    -.0825297    .0012661
         wb_18t24 |  -.0469161   .0300273    -1.56   0.119    -.1059341     .012102
         wb_25t34 |  -.0131496   .0199514    -0.66   0.510    -.0523638    .0260646
         wb_35t44 |  -.0216672   .0243474    -0.89   0.374    -.0695216    .0261873
         wb_45t59 |  -.0167508   .0220928    -0.76   0.449    -.0601739    .0266723
          avg_dur |   .0034585   .0252599     0.14   0.891    -.0461895    .0531065
          hh_kids |  -.0920842   .0423983    -2.17   0.030    -.1754174   -.0087511
mpreis_flats_rent |   .0234777   .0278479     0.84   0.400    -.0312569    .0782123
            _cons |  -2.647343   10.53788    -0.25   0.802    -23.35938    18.06469
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------------+
        Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------------+---------------------------------------|
   wahl_id#stadtbez |       200           1         199     |
             sb_new |       432         432           0    *|
------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |         94         94

        |        Observations
        |      total   distinct
--------+----------------------
 sb_new |       2704        338

.         
.         * TABLE E9. Balanced Sample Results–Effects by Distance
.         outreg using "$tables/Table_E9_ES_bal_samples_dist2", replay tex replace fragment
(note: file //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/tables/Table_E9
> _ES_bal_samples_dist2.tex not found)
  ----------------------------------------------------------------------------------------------
                                              (1)             (2)                  (3)         
                                                                                               
  ----------------------------------------------------------------------------------------------
   (N-)x\hspace{.7cm}Reassignment (#t-4#)    -0.18           -0.19                -0.13        
                                             (0.30)         (0.30)               (0.31)        
   (N-)x\hspace{.7cm}Reassignment (#t-3#)    -0.26           -0.28                0.04         
                                             (0.29)         (0.29)               (0.33)        
   (N-)x\hspace{.7cm}Reassignment (#t-2#)    -0.26           -0.26                -0.00        
                                             (0.23)         (0.23)               (0.26)        
   (N-)x\hspace{.7cm}Reassignment (#t+0#)     0.31           0.29                 0.17         
                                             (0.37)         (0.38)               (0.44)        
   (N-)x\hspace{.7cm}Reassignment (#t+1#)     0.06           0.09                 0.14         
                                             (0.38)         (0.38)               (0.40)        
   (N-)x\hspace{.7cm}Reassignment (#t+2#)     0.46           0.47                 0.57         
                                             (0.38)         (0.39)               (0.39)        
   (N+)x\hspace{.7cm}Reassignment (#t-4#)     0.16           0.14                 0.06         
                                             (0.26)         (0.26)               (0.29)        
   (N+)x\hspace{.7cm}Reassignment (#t-3#)    -0.12           -0.15                -0.21        
                                             (0.27)         (0.26)               (0.31)        
   (N+)x\hspace{.7cm}Reassignment (#t-2#)     0.11           0.11                 0.23         
                                             (0.21)         (0.21)               (0.25)        
   (N+)x\hspace{.7cm}Reassignment (#t+0#)   -2.10***       -2.10***             -2.72***       
                                             (0.34)         (0.34)               (0.36)        
   (N+)x\hspace{.7cm}Reassignment (#t+1#)   -2.25***       -2.25***             -2.38***       
                                             (0.33)         (0.33)               (0.36)        
   (N+)x\hspace{.7cm}Reassignment (#t+2#)   -1.68***       -1.73***             -1.83***       
                                             (0.36)         (0.37)               (0.39)        
   R2                                         0.98           0.98                 0.98         
   N                                         3,904           3,872                3,552        
   Unbalanced sample                           X                                               
   Balanced sample                                    $ \tau \in [-4,0] $  $ \tau \in [-2,1] $ 
   #treated precincts                         150            146                  106          
   #control precincts                         338             338                  338         
  ----------------------------------------------------------------------------------------------


      -------------------------------------------------------------------------------------
                                                                   (4)                    
                                                \multicolumn{6}{c}{Polling Place Turnout} 
      -------------------------------------------------------------------------------------
       (N-)x\hspace{.7cm}Reassignment (#t-4#)                     -0.14                   
                                                                 (0.32)                   
       (N-)x\hspace{.7cm}Reassignment (#t-3#)                     0.04                    
                                                                 (0.35)                   
       (N-)x\hspace{.7cm}Reassignment (#t-2#)                     0.02                    
                                                                 (0.27)                   
       (N-)x\hspace{.7cm}Reassignment (#t+0#)                     0.15                    
                                                                 (0.46)                   
       (N-)x\hspace{.7cm}Reassignment (#t+1#)                     0.15                    
                                                                 (0.44)                   
       (N-)x\hspace{.7cm}Reassignment (#t+2#)                     0.62                    
                                                                 (0.42)                   
       (N+)x\hspace{.7cm}Reassignment (#t-4#)                     0.01                    
                                                                 (0.29)                   
       (N+)x\hspace{.7cm}Reassignment (#t-3#)                     -0.25                   
                                                                 (0.32)                   
       (N+)x\hspace{.7cm}Reassignment (#t-2#)                     0.24                    
                                                                 (0.26)                   
       (N+)x\hspace{.7cm}Reassignment (#t+0#)                   -2.88***                  
                                                                 (0.37)                   
       (N+)x\hspace{.7cm}Reassignment (#t+1#)                   -2.53***                  
                                                                 (0.37)                   
       (N+)x\hspace{.7cm}Reassignment (#t+2#)                   -1.88***                  
                                                                 (0.41)                   
       R2                                                         0.98                    
       N                                                          3,504                   
       Unbalanced sample                                                                  
       Balanced sample                                     $ \tau \in [-4,1] $            
       #treated precincts                                         100                     
       #control precincts                                          338                    
      -------------------------------------------------------------------------------------


  ---------------------------------------------------------------------------------------------
                                                    (5)                  (6)            (7)   
                                                                                              
  ---------------------------------------------------------------------------------------------
   (N-)x\hspace{.7cm}Reassignment (#t-4#)          0.02                 0.02           -0.24  
                                                  (0.31)               (0.32)         (0.28)  
   (N-)x\hspace{.7cm}Reassignment (#t-3#)          0.18                 0.19           0.34   
                                                  (0.34)               (0.36)         (0.25)  
   (N-)x\hspace{.7cm}Reassignment (#t-2#)          0.06                 0.09           -0.03  
                                                  (0.27)               (0.28)         (0.22)  
   (N-)x\hspace{.7cm}Reassignment (#t+0#)          0.42                 0.41           -0.06  
                                                  (0.44)               (0.46)         (0.36)  
   (N-)x\hspace{.7cm}Reassignment (#t+1#)          0.38                 0.41           0.24   
                                                  (0.40)               (0.43)         (0.37)  
   (N-)x\hspace{.7cm}Reassignment (#t+2#)          0.69                 0.75           0.40   
                                                  (0.40)               (0.42)         (0.42)  
   (N+)x\hspace{.7cm}Reassignment (#t-4#)          0.10                 0.05           -0.19  
                                                  (0.31)               (0.31)         (0.26)  
   (N+)x\hspace{.7cm}Reassignment (#t-3#)          -0.04                -0.09          0.15   
                                                  (0.30)               (0.31)         (0.22)  
   (N+)x\hspace{.7cm}Reassignment (#t-2#)          0.27                 0.28           -0.17  
                                                  (0.25)               (0.27)         (0.18)  
   (N+)x\hspace{.7cm}Reassignment (#t+0#)        -2.74***             -2.92***        1.06**  
                                                  (0.38)               (0.39)         (0.33)  
   (N+)x\hspace{.7cm}Reassignment (#t+1#)        -2.50***             -2.66***        1.74*** 
                                                  (0.38)               (0.39)         (0.33)  
   (N+)x\hspace{.7cm}Reassignment (#t+2#)        -1.83***             -1.88***        1.70*** 
                                                  (0.39)               (0.41)         (0.38)  
   R2                                              0.98                 0.98           0.96   
   N                                               3,504                3,456          3,904  
   Unbalanced sample                                                                    X     
   Balanced sample                          $ \tau \in [-2,2] $  $ \tau \in [-4,2] $          
   #treated precincts                              100                   94            150    
   #control precincts                               338                  338            338   
  ---------------------------------------------------------------------------------------------


       ------------------------------------------------------------------------------------
                                                         (8)                  (9)         
                                                                                          
       ------------------------------------------------------------------------------------
        (N-)x\hspace{.7cm}Reassignment (#t-4#)          -0.24                0.03         
                                                       (0.28)               (0.25)        
        (N-)x\hspace{.7cm}Reassignment (#t-3#)          0.34                 0.08         
                                                       (0.25)               (0.30)        
        (N-)x\hspace{.7cm}Reassignment (#t-2#)          -0.04                -0.08        
                                                       (0.22)               (0.24)        
        (N-)x\hspace{.7cm}Reassignment (#t+0#)          -0.09                0.48         
                                                       (0.36)               (0.42)        
        (N-)x\hspace{.7cm}Reassignment (#t+1#)          0.19                 0.34         
                                                       (0.37)               (0.39)        
        (N-)x\hspace{.7cm}Reassignment (#t+2#)          0.43                 0.58         
                                                       (0.42)               (0.44)        
        (N+)x\hspace{.7cm}Reassignment (#t-4#)          -0.17                -0.09        
                                                       (0.26)               (0.24)        
        (N+)x\hspace{.7cm}Reassignment (#t-3#)          0.17                 0.11         
                                                       (0.22)               (0.29)        
        (N+)x\hspace{.7cm}Reassignment (#t-2#)          -0.18                -0.17        
                                                       (0.18)               (0.21)        
        (N+)x\hspace{.7cm}Reassignment (#t+0#)         1.03**               1.85***       
                                                       (0.33)               (0.33)        
        (N+)x\hspace{.7cm}Reassignment (#t+1#)         1.69***              1.90***       
                                                       (0.33)               (0.35)        
        (N+)x\hspace{.7cm}Reassignment (#t+2#)         1.77***              1.95***       
                                                       (0.39)               (0.41)        
        R2                                              0.96                 0.96         
        N                                               3,872                3,552        
        Unbalanced sample                                                                 
        Balanced sample                          $ \tau \in [-4,0] $  $ \tau \in [-2,1] $ 
        #treated precincts                              146                  106          
        #control precincts                               338                  338         
       ------------------------------------------------------------------------------------


         -------------------------------------------------------------------------------
                                                                  (10)                 
                                                   \multicolumn{6}{c}{Mail-in turnout} 
         -------------------------------------------------------------------------------
          (N-)x\hspace{.7cm}Reassignment (#t-4#)                  -0.01                
                                                                 (0.26)                
          (N-)x\hspace{.7cm}Reassignment (#t-3#)                  0.02                 
                                                                 (0.31)                
          (N-)x\hspace{.7cm}Reassignment (#t-2#)                  -0.14                
                                                                 (0.26)                
          (N-)x\hspace{.7cm}Reassignment (#t+0#)                  0.43                 
                                                                 (0.44)                
          (N-)x\hspace{.7cm}Reassignment (#t+1#)                  0.35                 
                                                                 (0.41)                
          (N-)x\hspace{.7cm}Reassignment (#t+2#)                  0.55                 
                                                                 (0.47)                
          (N+)x\hspace{.7cm}Reassignment (#t-4#)                  -0.08                
                                                                 (0.24)                
          (N+)x\hspace{.7cm}Reassignment (#t-3#)                  0.12                 
                                                                 (0.30)                
          (N+)x\hspace{.7cm}Reassignment (#t-2#)                  -0.17                
                                                                 (0.22)                
          (N+)x\hspace{.7cm}Reassignment (#t+0#)                 1.96***               
                                                                 (0.34)                
          (N+)x\hspace{.7cm}Reassignment (#t+1#)                 1.97***               
                                                                 (0.36)                
          (N+)x\hspace{.7cm}Reassignment (#t+2#)                 1.94***               
                                                                 (0.42)                
          R2                                                      0.96                 
          N                                                       3,504                
          Unbalanced sample                                                            
          Balanced sample                                  $ \tau \in [-4,1] $         
          #treated precincts                                      100                  
          #control precincts                                       338                 
         -------------------------------------------------------------------------------


  ----------------------------------------------------------------------------------------------
                                                   (11)                 (12)            (13)   
                                                                                               
  ----------------------------------------------------------------------------------------------
   (N-)x\hspace{.7cm}Reassignment (#t-4#)          -0.06                -0.12          -0.42   
                                                  (0.26)               (0.27)          (0.32)  
   (N-)x\hspace{.7cm}Reassignment (#t-3#)          -0.02                -0.09           0.08   
                                                  (0.32)               (0.32)          (0.26)  
   (N-)x\hspace{.7cm}Reassignment (#t-2#)          -0.13                -0.20          -0.29   
                                                  (0.26)               (0.27)          (0.26)  
   (N-)x\hspace{.7cm}Reassignment (#t+0#)          0.31                 0.23            0.25   
                                                  (0.41)               (0.43)          (0.29)  
   (N-)x\hspace{.7cm}Reassignment (#t+1#)          0.15                 0.15            0.30   
                                                  (0.39)               (0.42)          (0.34)  
   (N-)x\hspace{.7cm}Reassignment (#t+2#)          0.45                 0.41           0.86**  
                                                  (0.45)               (0.48)          (0.32)  
   (N+)x\hspace{.7cm}Reassignment (#t-4#)          -0.24                -0.23          -0.03   
                                                  (0.23)               (0.23)          (0.26)  
   (N+)x\hspace{.7cm}Reassignment (#t-3#)          -0.02                -0.01           0.03   
                                                  (0.30)               (0.30)          (0.25)  
   (N+)x\hspace{.7cm}Reassignment (#t-2#)          -0.26                -0.27          -0.07   
                                                  (0.21)               (0.22)          (0.21)  
   (N+)x\hspace{.7cm}Reassignment (#t+0#)         1.93***              2.06***        -1.04*** 
                                                  (0.36)               (0.36)          (0.26)  
   (N+)x\hspace{.7cm}Reassignment (#t+1#)         1.91***              2.00***         -0.51   
                                                  (0.38)               (0.38)          (0.31)  
   (N+)x\hspace{.7cm}Reassignment (#t+2#)         1.88***              1.87***          0.02   
                                                  (0.41)               (0.43)          (0.30)  
   R2                                              0.96                 0.96            0.99   
   N                                               3,504                3,456          3,904   
   Unbalanced sample                                                                     X     
   Balanced sample                          $ \tau \in [-2,2] $  $ \tau \in [-4,2] $           
   #treated precincts                              100                   94             150    
   #control precincts                               338                  338            338    
  ----------------------------------------------------------------------------------------------


       ------------------------------------------------------------------------------------
                                                        (14)                 (15)         
                                                                                          
       ------------------------------------------------------------------------------------
        (N-)x\hspace{.7cm}Reassignment (#t-4#)          -0.43                -0.10        
                                                       (0.32)               (0.29)        
        (N-)x\hspace{.7cm}Reassignment (#t-3#)          0.07                 0.12         
                                                       (0.26)               (0.32)        
        (N-)x\hspace{.7cm}Reassignment (#t-2#)          -0.29                -0.08        
                                                       (0.27)               (0.30)        
        (N-)x\hspace{.7cm}Reassignment (#t+0#)          0.20                 0.66*        
                                                       (0.29)               (0.30)        
        (N-)x\hspace{.7cm}Reassignment (#t+1#)          0.28                 0.48         
                                                       (0.34)               (0.35)        
        (N-)x\hspace{.7cm}Reassignment (#t+2#)         0.90**               1.15***       
                                                       (0.32)               (0.32)        
        (N+)x\hspace{.7cm}Reassignment (#t-4#)          -0.02                -0.03        
                                                       (0.26)               (0.31)        
        (N+)x\hspace{.7cm}Reassignment (#t-3#)          0.03                 -0.10        
                                                       (0.25)               (0.30)        
        (N+)x\hspace{.7cm}Reassignment (#t-2#)          -0.07                0.06         
                                                       (0.21)               (0.26)        
        (N+)x\hspace{.7cm}Reassignment (#t+0#)        -1.07***              -0.87**       
                                                       (0.26)               (0.32)        
        (N+)x\hspace{.7cm}Reassignment (#t+1#)          -0.56                -0.49        
                                                       (0.31)               (0.34)        
        (N+)x\hspace{.7cm}Reassignment (#t+2#)          0.04                 0.12         
                                                       (0.31)               (0.33)        
        R2                                              0.99                 0.99         
        N                                               3,872                3,552        
        Unbalanced sample                                                                 
        Balanced sample                          $ \tau \in [-4,0] $  $ \tau \in [-2,1] $ 
        #treated precincts                              146                  106          
        #control precincts                               338                  338         
       ------------------------------------------------------------------------------------


--------------------------------------------------------------------------------------------------
                                                        (16)                        (17)         
                                          \multicolumn{6}{c}{Total turnout}                      
--------------------------------------------------------------------------------------------------
 (N-)x\hspace{.7cm}Reassignment (#t-4#)                 -0.15                       -0.03        
                                                       (0.30)                      (0.31)        
 (N-)x\hspace{.7cm}Reassignment (#t-3#)                 0.06                        0.16         
                                                       (0.34)                      (0.34)        
 (N-)x\hspace{.7cm}Reassignment (#t-2#)                 -0.12                       -0.07        
                                                       (0.33)                      (0.32)        
 (N-)x\hspace{.7cm}Reassignment (#t+0#)                 0.58                        0.72*        
                                                       (0.33)                      (0.31)        
 (N-)x\hspace{.7cm}Reassignment (#t+1#)                 0.50                        0.53         
                                                       (0.39)                      (0.37)        
 (N-)x\hspace{.7cm}Reassignment (#t+2#)                1.17***                     1.14***       
                                                       (0.34)                      (0.33)        
 (N+)x\hspace{.7cm}Reassignment (#t-4#)                 -0.06                       -0.14        
                                                       (0.32)                      (0.31)        
 (N+)x\hspace{.7cm}Reassignment (#t-3#)                 -0.13                       -0.06        
                                                       (0.30)                      (0.32)        
 (N+)x\hspace{.7cm}Reassignment (#t-2#)                 0.07                        0.01         
                                                       (0.27)                      (0.28)        
 (N+)x\hspace{.7cm}Reassignment (#t+0#)                -0.92**                     -0.81*        
                                                       (0.33)                      (0.34)        
 (N+)x\hspace{.7cm}Reassignment (#t+1#)                 -0.56                       -0.58        
                                                       (0.35)                      (0.37)        
 (N+)x\hspace{.7cm}Reassignment (#t+2#)                 0.06                        0.06         
                                                       (0.35)                      (0.34)        
 R2                                                     0.99                        0.99         
 N                                                      3,504                       3,504        
 Unbalanced sample                                                                               
 Balanced sample                                 $ \tau \in [-4,1] $         $ \tau \in [-2,2] $ 
 #treated precincts                                     100                         100          
 #control precincts                                      338                         338         
--------------------------------------------------------------------------------------------------


                 ---------------------------------------------------------------
                                                                  (18)         
                                                                               
                 ---------------------------------------------------------------
                  (N-)x\hspace{.7cm}Reassignment (#t-4#)          -0.09        
                                                                 (0.32)        
                  (N-)x\hspace{.7cm}Reassignment (#t-3#)          0.09         
                                                                 (0.36)        
                  (N-)x\hspace{.7cm}Reassignment (#t-2#)          -0.11        
                                                                 (0.35)        
                  (N-)x\hspace{.7cm}Reassignment (#t+0#)          0.65         
                                                                 (0.34)        
                  (N-)x\hspace{.7cm}Reassignment (#t+1#)          0.56         
                                                                 (0.41)        
                  (N-)x\hspace{.7cm}Reassignment (#t+2#)         1.17**        
                                                                 (0.35)        
                  (N+)x\hspace{.7cm}Reassignment (#t-4#)          -0.18        
                                                                 (0.31)        
                  (N+)x\hspace{.7cm}Reassignment (#t-3#)          -0.11        
                                                                 (0.33)        
                  (N+)x\hspace{.7cm}Reassignment (#t-2#)          0.01         
                                                                 (0.29)        
                  (N+)x\hspace{.7cm}Reassignment (#t+0#)         -0.86*        
                                                                 (0.36)        
                  (N+)x\hspace{.7cm}Reassignment (#t+1#)          -0.67        
                                                                 (0.37)        
                  (N+)x\hspace{.7cm}Reassignment (#t+2#)          -0.01        
                                                                 (0.36)        
                  R2                                              0.99         
                  N                                               3,456        
                  Unbalanced sample                                            
                  Balanced sample                          $ \tau \in [-4,2] $ 
                  #treated precincts                               94          
                  #control precincts                               338         
                 ---------------------------------------------------------------


.         cleantex "$tables/Table_E9_ES_bal_samples_dist2.tex"  ,  replace        

\begin{tabular}{lcccccccccccccccccc}
\toprule  & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) & (14)
>  & (15) & (16) & (17) & (18)\\
 &  &  &  & \multicolumn{6}{c}{Polling Place Turnout} &  &  &  &  &  & \multicolumn{6}{c}{Mail-in 
> turnout} &  &  &  &  &  & \multicolumn{6}{c}{Total turnout} &  & \\
 (N-)x\hspace{.7cm}Reassignment ($ t-4$) & -0.18 & -0.19 & -0.13 & -0.14 & 0.02 & 0.02 & -0.24 & -
> 0.24 & 0.03 & -0.01 & -0.06 & -0.12 & -0.42 & -0.43 & -0.10 & -0.15 & -0.03 & -0.09\\
 & (0.30) & (0.30) & (0.31) & (0.32) & (0.31) & (0.32) & (0.28) & (0.28) & (0.25) & (0.26) & (0.26
> ) & (0.27) & (0.32) & (0.32) & (0.29) & (0.30) & (0.31) & (0.32)\\
(N-)x\hspace{.7cm}Reassignment ($ t-3$) & -0.26 & -0.28 & 0.04 & 0.04 & 0.18 & 0.19 & 0.34 & 0.34 
> & 0.08 & 0.02 & -0.02 & -0.09 & 0.08 & 0.07 & 0.12 & 0.06 & 0.16 & 0.09\\
 & (0.29) & (0.29) & (0.33) & (0.35) & (0.34) & (0.36) & (0.25) & (0.25) & (0.30) & (0.31) & (0.32
> ) & (0.32) & (0.26) & (0.26) & (0.32) & (0.34) & (0.34) & (0.36)\\
(N-)x\hspace{.7cm}Reassignment ($ t-2$) & -0.26 & -0.26 & -0.00 & 0.02 & 0.06 & 0.09 & -0.03 & -0.
> 04 & -0.08 & -0.14 & -0.13 & -0.20 & -0.29 & -0.29 & -0.08 & -0.12 & -0.07 & -0.11\\
 & (0.23) & (0.23) & (0.26) & (0.27) & (0.27) & (0.28) & (0.22) & (0.22) & (0.24) & (0.26) & (0.26
> ) & (0.27) & (0.26) & (0.27) & (0.30) & (0.33) & (0.32) & (0.35)\\
(N-)x\hspace{.7cm}Reassignment ($ t+0$) & 0.31 & 0.29 & 0.17 & 0.15 & 0.42 & 0.41 & -0.06 & -0.09 
> & 0.48 & 0.43 & 0.31 & 0.23 & 0.25 & 0.20 & 0.66* & 0.58 & 0.72* & 0.65\\
 & (0.37) & (0.38) & (0.44) & (0.46) & (0.44) & (0.46) & (0.36) & (0.36) & (0.42) & (0.44) & (0.41
> ) & (0.43) & (0.29) & (0.29) & (0.30) & (0.33) & (0.31) & (0.34)\\
(N-)x\hspace{.7cm}Reassignment ($ t+1$) & 0.06 & 0.09 & 0.14 & 0.15 & 0.38 & 0.41 & 0.24 & 0.19 & 
> 0.34 & 0.35 & 0.15 & 0.15 & 0.30 & 0.28 & 0.48 & 0.50 & 0.53 & 0.56\\
 & (0.38) & (0.38) & (0.40) & (0.44) & (0.40) & (0.43) & (0.37) & (0.37) & (0.39) & (0.41) & (0.39
> ) & (0.42) & (0.34) & (0.34) & (0.35) & (0.39) & (0.37) & (0.41)\\
(N-)x\hspace{.7cm}Reassignment ($ t+2$) & 0.46 & 0.47 & 0.57 & 0.62 & 0.69 & 0.75 & 0.40 & 0.43 & 
> 0.58 & 0.55 & 0.45 & 0.41 & 0.86** & 0.90** & 1.15*** & 1.17*** & 1.14*** & 1.17**\\
 & (0.38) & (0.39) & (0.39) & (0.42) & (0.40) & (0.42) & (0.42) & (0.42) & (0.44) & (0.47) & (0.45
> ) & (0.48) & (0.32) & (0.32) & (0.32) & (0.34) & (0.33) & (0.35)\\
(N+)x\hspace{.7cm}Reassignment ($ t-4$) & 0.16 & 0.14 & 0.06 & 0.01 & 0.10 & 0.05 & -0.19 & -0.17 
> & -0.09 & -0.08 & -0.24 & -0.23 & -0.03 & -0.02 & -0.03 & -0.06 & -0.14 & -0.18\\
 & (0.26) & (0.26) & (0.29) & (0.29) & (0.31) & (0.31) & (0.26) & (0.26) & (0.24) & (0.24) & (0.23
> ) & (0.23) & (0.26) & (0.26) & (0.31) & (0.32) & (0.31) & (0.31)\\
(N+)x\hspace{.7cm}Reassignment ($ t-3$) & -0.12 & -0.15 & -0.21 & -0.25 & -0.04 & -0.09 & 0.15 & 0
> .17 & 0.11 & 0.12 & -0.02 & -0.01 & 0.03 & 0.03 & -0.10 & -0.13 & -0.06 & -0.11\\
 & (0.27) & (0.26) & (0.31) & (0.32) & (0.30) & (0.31) & (0.22) & (0.22) & (0.29) & (0.30) & (0.30
> ) & (0.30) & (0.25) & (0.25) & (0.30) & (0.30) & (0.32) & (0.33)\\
(N+)x\hspace{.7cm}Reassignment ($ t-2$) & 0.11 & 0.11 & 0.23 & 0.24 & 0.27 & 0.28 & -0.17 & -0.18 
> & -0.17 & -0.17 & -0.26 & -0.27 & -0.07 & -0.07 & 0.06 & 0.07 & 0.01 & 0.01\\
 & (0.21) & (0.21) & (0.25) & (0.26) & (0.25) & (0.27) & (0.18) & (0.18) & (0.21) & (0.22) & (0.21
> ) & (0.22) & (0.21) & (0.21) & (0.26) & (0.27) & (0.28) & (0.29)\\
(N+)x\hspace{.7cm}Reassignment ($ t+0$) & -2.10*** & -2.10*** & -2.72*** & -2.88*** & -2.74*** & -
> 2.92*** & 1.06** & 1.03** & 1.85*** & 1.96*** & 1.93*** & 2.06*** & -1.04*** & -1.07*** & -0.87*
> * & -0.92** & -0.81* & -0.86*\\
 & (0.34) & (0.34) & (0.36) & (0.37) & (0.38) & (0.39) & (0.33) & (0.33) & (0.33) & (0.34) & (0.36
> ) & (0.36) & (0.26) & (0.26) & (0.32) & (0.33) & (0.34) & (0.36)\\
(N+)x\hspace{.7cm}Reassignment ($ t+1$) & -2.25*** & -2.25*** & -2.38*** & -2.53*** & -2.50*** & -
> 2.66*** & 1.74*** & 1.69*** & 1.90*** & 1.97*** & 1.91*** & 2.00*** & -0.51 & -0.56 & -0.49 & -0
> .56 & -0.58 & -0.67\\
 & (0.33) & (0.33) & (0.36) & (0.37) & (0.38) & (0.39) & (0.33) & (0.33) & (0.35) & (0.36) & (0.38
> ) & (0.38) & (0.31) & (0.31) & (0.34) & (0.35) & (0.37) & (0.37)\\
(N+)x\hspace{.7cm}Reassignment ($ t+2$) & -1.68*** & -1.73*** & -1.83*** & -1.88*** & -1.83*** & -
> 1.88*** & 1.70*** & 1.77*** & 1.95*** & 1.94*** & 1.88*** & 1.87*** & 0.02 & 0.04 & 0.12 & 0.06 
> & 0.06 & -0.01\\
 & (0.36) & (0.37) & (0.39) & (0.41) & (0.39) & (0.41) & (0.38) & (0.39) & (0.41) & (0.42) & (0.41
> ) & (0.43) & (0.30) & (0.31) & (0.33) & (0.35) & (0.34) & (0.36)\\
$ R^2$  & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 & 0.96 & 0.96 & 0.96 & 0.96 & 0.96 & 0.96 & 0.99
>  & 0.99 & 0.99 & 0.99 & 0.99 & 0.99\\
Observations & 3,904 & 3,872 & 3,552 & 3,504 & 3,504 & 3,456 & 3,904 & 3,872 & 3,552 & 3,504 & 3,5
> 04 & 3,456 & 3,904 & 3,872 & 3,552 & 3,504 & 3,504 & 3,456\\
Unbalanced sample & $\times$  &  &  &  &  &  & $\times$  &  &  &  &  &  & $\times$  &  &  &  &  & 
> \\
Balanced sample &  & $ \tau \in [-4,0] $ & $ \tau \in [-2,1] $ & $ \tau \in [-4,1] $ & $ \tau \in 
> [-2,2] $ & $ \tau \in [-4,2] $ &  & $ \tau \in [-4,0] $ & $ \tau \in [-2,1] $ & $ \tau \in [-4,1
> ] $ & $ \tau \in [-2,2] $ & $ \tau \in [-4,2] $ &  & $ \tau \in [-4,0] $ & $ \tau \in [-2,1] $ &
>  $ \tau \in [-4,1] $ & $ \tau \in [-2,2] $ & $ \tau \in [-4,2] $\\
\#treated precincts & 150  & 146  & 106  & 100  & 100  & 94  & 150  & 146  & 106  & 100  & 100  & 
> 94  & 150  & 146  & 106  & 100  & 100  & 94 \\
\#control precincts & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 & 338 
> & 338 & 338 & 338 & 338 & 338\\
\bottomrule\end{tabular}


.         
. 
end of do-file
Running: 05_school_construction_programm_stats.do

. /*
> 
> Input: $rawdata/schuloffensive/Wahllokale_merge.xlsx [school polling places with renovation info
> rmation]
> 
> Stat on relation of school renovation programm ("schuloffensive") and polling place unavailabili
> ty
> 
> https://risi.muenchen.de/risi/ [accessed November 2023]
> 
> */
. 
. *import cases and save unique polling places with construction work after 2014
.         tempfile pp_merge

.         import excel "$rawdata/schuloffensive/Wahllokale_merge.xlsx", clear first sheet("schulof
> fensive processed")
(8 vars, 34 obs)

.         bys wl_id (wahl_id): keep if _n==1
(1 observation deleted)

.         keep wl_id

.         save `pp_merge'
file C:\Users\Alipour\AppData\Local\Temp\9\ST_77c8_000001.tmp saved as .dta format

. 
. 
. * how many (and which) schools went from active to inactive between 2014 and 2020?
.         use "$newdata/wahllokal_change.dta", clear

.         distinct wl_id // 299 distinct polling places

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |       2691        299

. 
. * KEEP: Schools going from active to inactive since 2017
.         keep if wl_dropped==1 & wahl_id>=5 & wl_school==1
(2,635 observations deleted)

.         distinct wl_id // 54 distinct polling places that are schools and with relevant change s
> ince 2017

       |        Observations
       |      total   distinct
-------+----------------------
 wl_id |         56         54

.         bys wl_id (wahl_id): keep if _n==1
(2 observations deleted)

. 
. * merge with construction plan
.         merge m:1 wl_id using `pp_merge', assert(1 3)

    Result                      Number of obs
    -----------------------------------------
    Not matched                            21
        from master                        21  (_merge==1)
        from using                          0  (_merge==2)

    Matched                                33  (_merge==3)
    -----------------------------------------

. 
. * % part of the programm (_merge==3) ?
.         tab _merge // 61%

   Matching result from |
                  merge |      Freq.     Percent        Cum.
------------------------+-----------------------------------
        Master only (1) |         21       38.89       38.89
            Matched (3) |         33       61.11      100.00
------------------------+-----------------------------------
                  Total |         54      100.00

.         
. 
end of do-file
Running: 06_conceptual_framework_figures_b2_b3.do

. /*
> 
> Illustration of theretical model
> 
> Output: Figures B.2, B.3
> 
> */
. 
. cap program drop fcts 

. program fcts, rclass
  1.         
.         /* draw functions depending on model parameters */ 
.         
.         // thresholds
.         local z0 "(b-u)/g"
  2.         local z1 "(b-u-e)/(g*x)"
  3.         local u0 "b/g"
  4.         local u1 "(b-e)/(g*x)"
  5.         
.         // discontinuities
.         return local null       = (b-u-e)/g // null of mail-in curve    
  6.         return local q1 = (b-u-e)/(b-u)
  7.         return local s  = (b-e)/b
  8.         return local q2 = (b-e)/(b-u)
  9. 
.         * Polling place
.                 // t=0
.                 return local P0         "(`z0'+(1-a)*(`u0'-`z0'))"
 10. 
.                 // t=1 (no IA)
.                 return local P1no       "(`z1'+(1-a)*(`u1'-`z1'))"
 11. 
.                 // t=1 inattention
.                 return local P1_1 "( `z1'+a*t*(`u1'-`z1')+(1-a)*(`u1'-`z1'))"
 12.                 return local P1_2 "( `z1'+a*t*(`z0'-`z1') + (1-a)*(`u1'-`z1'))"
 13.                 return local P1_3 "( `z1'+a*t*(`z0'-`z1')+(1-a)*(`u1'-`z1') - p*(1-a)*(`u1'-`
> u0') )"
 14.                 return local P1_4 "( `z1'+(1-a)*(`u1'-`z1') - p*(1-a)*(`u1'-`u0'))"
 15.                 
.         * Mail-in
.                 // t=0
.                 return local M0 "(a*(1-`z0'))"
 16.         
.                 // t=1 (no IA)
.                 return local M1no "a*(1-`z1')"
 17.                 
.                 // t=1 inattention
.                 return local M1_1 "(a*(1-`z1')-t*a*(`z0'-`z1'))"
 18.                 return local M1_2 "(a*(1-`z1'))"                 
 19.                 
.         * total
.                 // t=0
.                 return local T0 "(`u0'+a*(1-`u0'))"
 20.                 
.                 // t=1 (no IA)
.                 return local T1no       "(`u1'+a*(1-`u1'))"
 21.                 
.                 // t=1 inattention
.                 return local T1_1       "(`u1'+(1-t)*a*(`z0'-`u1')+a*(1-`z0'))"
 22.                 return local T1_2       "(`u1'+a*(1-`u1'))"
 23.                 return local T1_3       "(`u1'+a*(1-`u1')-p*(1-a)*(`u1'-`u0'))"
 24. end     

. 
. *** CASE 1: no inattention , t=0, p=0
. 
.  * Set model model parameters
.         scalar b                = 100   // Benefit

.         scalar g                = 160   // distance pref parameter, gamma, g > B !

.         scalar c                = 70    // cost of mail-in voting (low)

.         scalar e                = 60    // search cost, e < c !

.         scalar u                = b-c   // net utility mail-in

.         scalar t                = 0     // share inattentive polling place voters in period 0, P
> 0

.         scalar p                = 0             // share inattentive abstainers in period 0, A0

.         scalar a                = c/g + .1      // share mail-in voters, a > c/g ! (otherwise no
>  Mail in t=0)

.         assert inrange(a,0,1)

.         
.         // functions 
.         fcts 

.          
. 
. * Plot: Figure B2. No inattention
. tw      (function `r(T1no)'/`r(T0)', range(0.08 2)                              lcol(black) lpat
> (solid))        ///
>         (function `r(T1_3)'/`r(T0)', range(0.08 `r(s)')                 lcol(black) lpat(-....-)
> )       ///
>         (function `r(T1_2)'/`r(T0)', range(`r(s)' `r(q2)')              lcol(black) lpat(-....-)
> )       ///
>         (function `r(T1_1)'/`r(T0)', range(`r(q2)' 2)                   lcol(black) lpat(-....-)
> )       ///     
>         (function `r(M1no)'/`r(M0)', range(`r(null)' 2)                 lcol(cranberry) lpat(sol
> id))    ///
>         (function `r(M1_2)'/`r(M0)', range(`r(null)' `r(q1)')   lcol(cranberry) lpat(-....-)) //
> /
>         (function `r(M1_1)'/`r(M0)', range(`r(q1)' 2)                   lcol(cranberry) lpat(-..
> ..-)) ///
>         (function 0*x, range(0 `r(null)' )                                              lcol(cra
> nberry) lpat(solid)) ///                
>         (function `r(P1no)'/`r(P0)', range(0.08 2)                              lcol(blue) lpat(
> solid))         ///
>         (function `r(P1_4)'/`r(P0)', range(0.08 `r(q1)')                lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_3)'/`r(P0)', range(`r(q1)' `r(s)')              lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_2)'/`r(P0)', range(`r(s)' `r(q2)')              lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_1)'/`r(P0)', range(`r(q2)' 2)                   lcol(blue) lpat(-....-))
>  ///
>         , legend(size(small) order(1 "total " 5 "mail"  9 "in person") pos(1) ring(0) row(3) tit
> le("Turnout")) ///
>         ytitle("Turnout in Period 1 relative to Period 0",size(medium)) xtitle("Distance in Peri
> od 1 relative to Period 0", size(medium)) ///
>         xlabel(0 "0" 1 "1" 2 " ") ylabel(0 "0" 1 "1" 4 " ") ///
>         xline(1, lpat(solid) lcol(gray%50)) yline(1, lpat(solid) lcol(gray%50)) yla(,nogrid) xla
> (,nogrid)

.         gr_edit .legend.title.DragBy 0 -4

.         graph export "$figures/Figure_B2_theory_rel_turnout_noIA.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_B2_the
    > ory_rel_turnout_noIA.pdf saved as PDF format

. 
.         
.         
. *** CASE 2: only in-person voters inattentive , t>0, p=0
. 
.  * Set model model parameters
.         scalar b                = 100   // Benefit

.         scalar g                = 160   // distance pref parameter, gamma, g > B !

.         scalar c                = 70    // cost of mail-in voting (low)

.         scalar e                = 60    // search cost, e < c !

.         scalar u                = b-c   // net utility mail-in

.         scalar t                = 0.5   // share inattentive polling place voters in period 0, P
> 0       

.         scalar p                = 0             // share inattentive abstainers in period 0, A0

.         scalar a                = c/g + .1      // share mail-in voters, a > c/g ! (otherwise no
>  Mail in t=0)

.         assert inrange(a,0,1)

.         
.         // functions 
.         fcts 

. 
. * Plot: Figure B3a. Only pp voters inattentive
. tw      (function `r(T1no)'/`r(T0)', range(0.08 2)                              lcol(black) lpat
> (solid))        ///
>         (function `r(T1_3)'/`r(T0)', range(0.08 `r(s)')                 lcol(black) lpat(-....-)
> )       ///
>         (function `r(T1_2)'/`r(T0)', range(`r(s)' `r(q2)')              lcol(black) lpat(-....-)
> )       ///
>         (function `r(T1_1)'/`r(T0)', range(`r(q2)' 2)                   lcol(black) lpat(-....-)
> )       ///     
>         (function `r(M1no)'/`r(M0)', range(`r(null)' 2)                 lcol(cranberry) lpat(sol
> id))    ///
>         (function `r(M1_2)'/`r(M0)', range(`r(null)' `r(q1)')   lcol(cranberry) lpat(-....-)) //
> /
>         (function `r(M1_1)'/`r(M0)', range(`r(q1)' 2)                   lcol(cranberry) lpat(-..
> ..-)) ///
>         (function 0*x, range(0 `r(null)' )                                              lcol(cra
> nberry) lpat(solid)) ///                
>         (function `r(P1no)'/`r(P0)', range(0.08 2)                              lcol(blue) lpat(
> solid))         ///
>         (function `r(P1_4)'/`r(P0)', range(0.08 `r(q1)')                lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_3)'/`r(P0)', range(`r(q1)' `r(s)')              lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_2)'/`r(P0)', range(`r(s)' `r(q2)')              lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_1)'/`r(P0)', range(`r(q2)' 2)                   lcol(blue) lpat(-....-))
>  ///
>         , legend(size(small) order(1 "total (no IA)" 2 "total (IA)" 5 "mail (no IA)" 6 "by mail 
> (IA)" 9 "in person (no IA)" 10 "in person (IA)") pos(1) ring(0) row(3) title("Turnout")) ///
>         ytitle("Turnout in Period 1 relative to Period 0",size(medium)) xtitle("Distance in Peri
> od 1 relative to Period 0", size(medium)) ///
>         xlabel(0 "0" 1 "1" 2 " ") ylabel(0 "0" 1 "1" 4 " ") ///
>         xline(1, lpat(solid) lcol(gray%50)) yline(1, lpat(solid) lcol(gray%50)) yla(,nogrid) xla
> (,nogrid)

.         graph export "$figures/Figure_B3a_theory_rel_turnout_IA1.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_B3a_th
    > eory_rel_turnout_IA1.pdf saved as PDF format

.         
.         
. 
. *** CASE 3: inattentive in-person voters and abstainers, t>0, p>0
. 
.  * Set model model parameters
.         scalar b                = 100   // Benefit

.         scalar g                = 160   // distance pref parameter, gamma, g > B !

.         scalar c                = 70    // cost of mail-in voting (low)

.         scalar e                = 60    // search cost, e < c !

.         scalar u                = b-c   // net utility mail-in

.         scalar t                = 0.5   // share inattentive polling place voters in period 0, P
> 0

.         scalar p                = 0.2           // share inattentive abstainers in period 0, A0

.         scalar a                = c/g + .1      // share mail-in voters, a > c/g ! (otherwise no
>  Mail in t=0)

.         assert inrange(a,0,1)

.         
.         // functions 
.         fcts 

. 
. * Plot: Figure B3b. abstainers and pp voters inattentive
. tw      (function `r(T1no)'/`r(T0)', range(0.08 2)                              lcol(black) lpat
> (solid))        ///
>         (function `r(T1_3)'/`r(T0)', range(0.08 `r(s)')                 lcol(black) lpat(-....-)
> )       ///
>         (function `r(T1_2)'/`r(T0)', range(`r(s)' `r(q2)')              lcol(black) lpat(-....-)
> )       ///
>         (function `r(T1_1)'/`r(T0)', range(`r(q2)' 2)                   lcol(black) lpat(-....-)
> )       ///     
>         (function `r(M1no)'/`r(M0)', range(`r(null)' 2)                 lcol(cranberry) lpat(sol
> id))    ///
>         (function `r(M1_2)'/`r(M0)', range(`r(null)' `r(q1)')   lcol(cranberry) lpat(-....-)) //
> /
>         (function `r(M1_1)'/`r(M0)', range(`r(q1)' 2)                   lcol(cranberry) lpat(-..
> ..-)) ///
>         (function 0*x, range(0 `r(null)' )                                              lcol(cra
> nberry) lpat(solid)) ///                
>         (function `r(P1no)'/`r(P0)', range(0.08 2)                              lcol(blue) lpat(
> solid))         ///
>         (function `r(P1_4)'/`r(P0)', range(0.08 `r(q1)')                lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_3)'/`r(P0)', range(`r(q1)' `r(s)')              lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_2)'/`r(P0)', range(`r(s)' `r(q2)')              lcol(blue) lpat(-....-))
>  ///
>         (function `r(P1_1)'/`r(P0)', range(`r(q2)' 2)                   lcol(blue) lpat(-....-))
>  ///
>         , legend(size(small) order(1 "total (no IA)" 2 "total (IA)" 5 "mail (no IA)" 6 "by mail 
> (IA)" 9 "in person (no IA)" 10 "in person (IA)") pos(1) ring(0) row(3) title("Turnout")) ///
>         ytitle("Turnout in Period 1 relative to Period 0",size(medium)) xtitle("Distance in Peri
> od 1 relative to Period 0", size(medium)) ///
>         xlabel(0 "0" 1 "1" 2 " ") ylabel(0 "0" 1 "1" 4 " ") ///
>         xline(1, lpat(solid) lcol(gray%50)) yline(1, lpat(solid) lcol(gray%50)) yla(,nogrid) xla
> (,nogrid)

.         graph export "$figures/Figure_B3b_theory_rel_turnout_IA2.pdf", replace
file
    //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/figures/Figure_B3b_th
    > eory_rel_turnout_IA2.pdf saved as PDF format

. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

.         log close 
      name:  <unnamed>
       log:  //vmpfs-int/ag-int$/05_Forschungsprojekte/voting/Replication/07_output/logs/3_analysi
> s.log
  log type:  text
 closed on:   3 Mar 2024, 13:28:39
--------------------------------------------------------------------------------------------------
